Predictive Analytics in Healthcare

A target of 90-95% levels of immunization coverage is necessary for the sustained control of vaccine-preventable diseases. In Nigeria, achieving and maintaining a high level of 90-95% immunization coverage which is an indication of the effectiveness of a health care system has remained elusive till date. This paper is therefore aimed at exploring the possibility of maintaining high levels of immunization coverage in Edo state using best subset binary logistic regression model and the chi-square test as validation tool. We found that the obtained logistic model which constitutes only five key predictor variables out of eight potential predictors used had a good predictive performance. In addition, validation of the five key predictor variables using the chisquare test shows that all had significant association in relation to a child completion of immunization schedule.

This document presents a systems-level framework for extending human life through continuous monitoring, predictive diagnostics, and parallel biological support systems.
The traditional medical paradigm is fundamentally reactive: intervention occurs after dysfunction or disease has already manifested. In contrast, this work proposes a shift toward a proactive, engineering-based model in which the human body is treated as a complex, dynamic system that can be continuously observed, modeled, and supported.
At its core, the approach does not aim to replace biological function, but to augment it. The human organism remains the central system, while external layers of sensing, computation, and actuation operate in parallel to stabilize, correct, and optimize physiological processes in real time.
The framework integrates four key principles:
• Continuous physiological sensing across multiple subsystems
• Real-time data analysis and predictive modeling using AI
• Closed-loop feedback control between detection and intervention
• Multi-layered safety mechanisms ensuring controlled and bounded operation
Rather than defining health as a static state, this model defines it as a maintained equilibrium within a continuously monitored system. Likewise, aging is treated not as a singular biological event, but as a progressive system-level degradation that may be managed, slowed, or partially reversed through targeted intervention and maintenance strategies.
This work is conceptual in nature and aims to establish an architectural foundation for future biomedical engineering systems that combine biology, computation, and control theory into a unified operational framework.

Cardiovascular diseases (CVDs) can be considered a severe concern to the universal health that affects the mortality rates. Clinical decision-making and early diagnosis can be challenged with the help of intelligent systems, which are based on data-driven models. The suggested solution would utilize the DenseNet-based deep learning model in making accurate predictions of heart disease using UCI Cleveland dataset. The data undergoes pre-processing with systematic data treatment of missing values, removal of duplicates, feature selection via correlation and standardization. In terms of predicting cardiac disease, the proposed model performs exceptionally well, with an accuracy of 99.65, precision of 98.38, recall of 98.77, and F1-score of 97.96. To provide comparative analysis, conventional machine learning classifiers such as KNN, MLP and Logistic Regression. The effectiveness model as demonstrated by the comprehensive performance gains indicate a high level of capturing of complex clinical patterns, which is effective in screening early cardiovascular risks in healthcare predictive analytics and is able to maintain model interpretability, hence making it a promising solution in early and automated heart disease detection in clinical environment.

This project explores the intersection of Medicine and Computer Science through an analytical study of first aid awareness in Alexandria, Egypt. Inspired by the CS50 methodology, this work aims to bridge the gap between emergency medical response and algorithmic thinking.

Artificial Intelligence (AI) is increasingly used in clinical decision support, yet most systems remain optimized for single-disease scenarios, limiting their usefulness for individuals managing Discordant Chronic Conditions (DCCs). This study examines how AI can better support multimorbidity through a qualitative evaluation of three tools-PaLM 2, the DCCs Interactive User Interface (iU), and Patients Like Me. Using a novel DCC-focused evaluation framework and a dual-stakeholder lens, we analyze insights from both patients and healthcare providers. Findings show that while AI can ease cognitive workload and enhance personalized guidance, users consistently report barriers including interpretability issues, information overload, and conflicts with patient goals. We translate these insights into actionable, user-informed design principles for AI systems that support complex trade-offs and care coordination. The work provides a human-centered foundation for building AI tools that meaningfully address the realities of multimorbidity.

Artificial intelligence (AI) is witnessing an evolutionary change onco-critical care practice due to its ability to guide clinical decisions on the critically ill cancer patient population, enabling the delivery of precise, data-driven, and proactive care. It is a distinctly complicated area because it incorporates the pathophysiology of malignancy and acute organ dysfunction, the toxicity caused by the treatment and the infectious or inflammatory complications. Although useful, traditional scoring instruments and clinical assessment devices are constrained in the ability to represent nonlinear and dynamic interplay that is inherent in this population. State-of-art AI techniques and models, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), can provide remedies when they generate high-dimensional multimodal data using electronic health records, medical imaging, physiological waveforms, laboratory metrics, and molecular profiles. Recent data prove that AI models can result in a higher rate of timely oncologic crisis, clinical prognostication, clinical worsening, and individual treatment of the patient in the intensive care unit. Clinical decision support systems that are powered by AI maximize real-time risk, support antimicrobial and anticancer therapy, and ventilatory and hemodynamic support and reduce clinician mental load and care variation. Multimodal predictive models that combine both structured and unstructured data also enable prognostic forecasting of symptoms and resource optimization to enhance patient outcomes and the ICU efficiency. However, there are still challenges such as data quality, interpretability, ethical concerns and integration into clinical processes. The safe implementation requires addressing the issues of algorithmic bias, guaranteeing transparency, and keeping clinicians supervising the implementation. Together with the clinical experience, AI promises to be a powerful augmentative intervention in onco-critical care, fulfilling contemporary high-value, timely, and personalized care to the most acute oncology patients.

Artificial intelligence (AI) is increasingly positioned as a transformative force in healthcare. The translation of AI from technical validation to real-world clinical impact remains a critical challenge. This systematic review aims to synthesize the evidence on the AI translational pathway in healthcare, focusing on the systemic barriers and facilitators to integration. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, Web of Science, and IEEE Xplore for studies published between 2000 and 2025. We included peer-reviewed original research, clinical trials, observational studies, and reviews reporting on AI technical validation, clinical deployment, implementation outcomes, or ethical governance. While AI models consistently demonstrate high diagnostic accuracy (92-98% in radiology) and robust predictive performance (AUC 0.76-0.82 in readmission forecasting), clinical adoption remains limited, with only 15-25% of departments integrating AI tools and approximately 60% of projects failing beyond pilot testing. Key barriers include interoperability limitations affecting over half of implementations, lack of clinician trust in unsupervised systems (35%), and regulatory immaturity, with only 27% of countries establishing AI governance frameworks. Moreover, performance disparities exceeding 10% were identified in 28% of models, alongside a pronounced global divide, as 73% of low-resource health systems lack enabling infrastructure. These findings underscore the need for systemic, trustworthy, and equity-driven AI integration strategies.

Artificial Intelligence (AI) has emerged as a transformative tool in modern healthcare, giving new capabilities for real-time study and prediction of
physiological trends. Homeostasis, the dynamic management of internal biological factors such as blood pressure, glucose concentration, core temperature,
and pH, is crucial to preserving health. Disruption in homeostatic homeostasis often serves as an early sign of pathological diseases. Conventional diagnostic
procedures generally uncover diseases only after clinical symptoms show, resulting in delayed interventions. In contrast, AI-enabled solutions, through
integration with biosensors and Internet of Things (IoT) devices, facilitate continuous monitoring and early recognition of small physiological anomalies.
Machine learning (ML) and deep learning (DL) algorithms can find non-linear connections within multidimensional datasets, aiding in the early diagnosis of
cardiovascular problems, diabetes mellitus, sepsis, and neurodegenerative diseases by examining patterns in vital signs and behavioral data. Despite these
gains, difficulties exist, including data heterogeneity, algorithmic bias, and limitations in training datasets, especially in marginalized populations. The
interpretability of complicated AI models remains a major challenge, often limiting clinical acceptance. Additionally, legislative limits and concerns over data
privacy provide impediments to wider implementation. Methodological shortcomings, such as the lack of defined data gathering techniques and insufficient
longitudinal research, limit the generalizability of AI findings. Future directions should focus on federated learning, individualized predictive models, and the
development of explainable AI frameworks. This paper critically explores present applications, technical limits, and future opportunities, emphasizing AI’s
promise to improve early disease diagnosis by monitoring homeostatic disruptions with precision and efficiency.

Artificial Intelligence (AI) systems are rapidly deployed in high-risk domains such as healthcare, finance, critical infrastructure, and autonomous mobility. Failures in these contexts can create irreversible harms at scale, amplifying the need for verifiable responsibility, fairness, transparency, and robustness. While many ethics guidelines have been proposed, organizations still lack a concrete, metrics-driven approach to design, assess, and monitor responsible AI in production. This paper presents a results-oriented, multi-layered governance framework-the Responsible AI Assurance Lifecycle (RAIAL)-that operationalizes principles from the EU AI Act, the U.S. Blueprint for an AI Bill of Rights, NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001:2023. We (i) perform a comparative analysis of major global AI regulatory and governance instruments, (ii) define a six-stage lifecycle connecting data, models, human oversight, and compliance, and (iii) empirically evaluate RAIAL on three representative high-risk use cases: a chest X-ray classifier, a credit scoring model, and an autonomous driving agent. Across domains, RAIAL yields tangible improvements: 18-42% gains in fairness metrics, 10-25% better calibration, and up to 45% reduction in safety-critical errors, while improving documentation and audit readiness. The framework is designed to be practical for regulators, data scientists, and governance teams seeking Q1-tier evidence of responsible AI deployment.

The integration of articial intelligence into autism spectrum disorder diagnosis holds tremendous potential for improving early detection and assessment accuracy, yet widespread clinical adoption remains limited by the black-box nature of complex machine learning models. This research presents a comprehensive Explainable AI framework specically designed for transparent autism diagnostic decisions that bridges the gap between computational predictions and clinical reasoning. Our

This study explores how patients and stakeholders envision integrated digital health systems. Background/Objectives: Integrating artificial intelligence (AI), wearable data, electronic health records (EHRs), and patient-reported outcomes could enable proactive and personalized healthcare. However, current solutions remain fragmented and poorly aligned with user expectations. This study aimed to explore patient and stakeholder needs for AI-driven integration and propose a conceptual framework to inform future system design. Methods: As part of the NSF Innovation Corps (I-Corps) program, we conducted semi-structured interviews with 44 participants representing Health Enthusiasts, Chronic Condition Managers, and Low-Engagement Users. Interviews followed the I-Corps customer discovery framework and were thematically analyzed using a hybrid deductive-inductive approach. Results: Participants highlighted four priorities: (i) interoperability and unification of data from wearables, EHRs, and self-reports; (ii) actionable personalization with predictive insights; (iii) trust and transparency in AI recommendations, often requiring clinician oversight; and (iv) usability through low-friction, intuitive interfaces. Age-and persona-specific differences emerged: younger participants favoring predictive features and older participants emphasizing safety, reassurance, and clinical integration. Conclusions: This exploratory qualitative study identified stakeholder needs that informed a conceptual framework for integrated digital health platforms. While preliminary, the framework provides a blueprint for future technical development and validation of patient-and provider-centered systems.

This work employs a multidisciplinary approach to identify research gaps in the existing literature by presenting a systematic review of systematic reviews on Explainable Artificial Intelligence (XAI). To the best of our knowledge, this is the first thorough meta-review that combines the findings of several excellent reviews to offer a more elevated viewpoint on the goals and difficulties facing the area. The review covers empirical studies published between 2021 and 2023, focusing on high-quality sources. An initial pool of 997 entries was screened across multiple databases, yielding 928 unique articles after duplicate removal. Ultimately, 14 studies met the inclusion criteria and were analyzed in depth. The quality assessment confirmed that all selected reviews adhered to established methodological standards. The key findings show XAI's broad uses, which range from increasing trust and transparency to assisting with financial and management decision-making. The prevalence of healthcare-focused studies emphasizes XAI's importance in enhancing interpretability, fairness, regulatory compliance, and personalized treatment options. Commonly used techniques include visual explanation tools, interpretable machine learning models, and model-agnostic approaches. While the review offers valuable insights, it acknowledges limitations such as its reliance on Q1 journals and the exclusion of broader sources, which may affect comprehensiveness. To advance the field, the study recommends expanding future research to underrepresented domains like autonomous vehicles, defense, and smart cities. It also calls for methodological innovation to enhance accessibility, fairness, privacy, and the development of intuitive explanation strategies. Addressing these gaps can significantly improve the trustworthiness and effectiveness of AI systems across sectors.

Artificial Intelligence (AI) is now used in many areas like healthcare, finance, education, and government services. But AI is not always fair. Sometimes it makes decisions that are biased because of the data or the way the system was built. This paper explains how AI can become biased, why fairness is important, and what steps are being taken to solve these problems. It also talks about explainable AI, new rules for safe AI, and ideas for the future.

Polypharmacy commonly defined as the concurrent use of five or more medications presents significant clinical risks, especially in multigenerational households where pediatric, adult, and geriatric care intersect. With increasing medication burdens and comorbidities, traditional methods of medication review and reconciliation are insufficient for timely and accurate risk stratification. This study evaluates the utility of machine learning (ML) algorithms in predicting polypharmacy-associated risks across diverse patient cohorts within shared household contexts. Drawing on anonymized electronic health records (EHRs) from family medicine, internal medicine, and pediatric care units, we developed and validated ensemble-based models that integrated medication histories, diagnostic codes, socioeconomic indicators, and household composition data. Our models achieved strong predictive performance, with area under the ROC curve (AUC) values exceeding 0.87 across age-stratified subgroups. We specifically examined the performance of random forests, gradient boosting machines, and neural networks in identifying medication interaction risks, hospitalization likelihoods, and early warning signs of adverse drug events (ADEs). Multigenerational dynamics such as caregiver stress, medication sharing, and uncoordinated prescribing were found to significantly influence risk scores. Pediatric risks were often underestimated in conventional screening tools, while elder populations showed higher susceptibility to anticholinergic burden and cumulative sedative effects. Our results highlight the importance of incorporating familial and generational context into predictive healthcare models. ML-based polypharmacy risk stratification can augment care coordination across departments and improve anticipatory interventions, especially in under-resourced

This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the potential to change the outlook in personalized medicine. Chronic diseases have become widespread and offer great challenges to the world's healthcare system. It is calling for potential approaches to diagnosis and treatment. It is a revolutionary concept that predictive analytical model underpinned by AI that helps healthcare risk forecast and introduce timely intercessions. The patient data analyzed using AI-based models, which can thereby generate specific information used to make much more accurate diagnosis and treatment plans. This research is an exemplar of big data. It aims at using algorithms in machine learning to process EHRs, genotype and phenotype data and lifestyle data among individuals in the world. The data are used for the educational process of building the predictive models, which assists in the early diagnosis of chronic diseases. It includes diabetes, cardiovascular diseases, cancer and others. The methods are used to supervised learning ranging from decision tree algorithms and support vector machines to neural networks where required. The real-time data analysis is included in the study for the purpose of real-time monitoring and risk analysis for proper healthcare preventative methodologies. The insights derived show that AI-advanced analytics of the chronic disease patients' data hold massive opportunities to redefine chronic illness treatment. AI for early detection of cases does not only lead to better care of patients but also helps lower health care expenditures since fewer intensive treatments are required. The proactive approach in connection with the individualization of treatment with the help of intelligent analytics is aimed at providing patient-tailored care.

Neurological diseases affect over one billion people globally, yet countless communities still lack basic access to specialist care, especially during emergencies. This work presents the first unified artificial intelligence platform capable of detecting and analyzing four major neurological conditions (brain tumors, strokes, Alzheimer's disease, and multiple sclerosis) within a single, web-accessible system. SeruNet platform addresses critical gaps in neurological care delivery by integrating condition-specific expert systems with advanced explainable AI techniques including one of the first documented application of XRAI (eXplanation with Region Attribution Integration) for neurological imaging. The unified architecture combines 2D and 3D analysis capabilities across multiple imaging modalities while maintaining specialized accuracy for each neurological condition. Key innovations include region-based attribution that aligns with clinical reasoning, a novel two-stage multiple sclerosis risk prediction model, and comprehensive bias-aware monitoring systems. Web-based deployment eliminates infrastructure barriers, enabling immediate access through standard browsers without specialized hardware requirements. This unified approach represents a paradigm shift from fragmented, condition-specific AI systems toward integrated, accessible neurological diagnostics designed for global health equity and immediate clinical deployment.

Alzheimer's disease is the most prevalent etiology of dementia, and its early diagnosis has been deemed instrumental for early intervention and better patient management. Over the last few years, deep learning techniques have been used with neuroimaging data, and notable gains in diagnostic performance have ensued. Such models have, however, typically been faulted for being "black boxes" that, despite their performance, hinder their acceptance in clinical practice due to a lack of transparency and interpretability. Current explainability methods have typically been used as post-hoc procedures, but they often yield inconsistent or anatomically irrelevant attribution maps that clinicians find hard to trust. In this review, progress in explainable AI for the detection of Alzheimer's is discussed, with a focus on DL methods combined with multimodal neuroimaging. Emphasis is given to XRAI, a region-based attribution method that has been demonstrated to produce more coherent and clinically interpretable explanations compared to conventional pixel-level techniques. The application of XRAI to 2D and 3D neuroimaging is considered, together with the potential of XRAI to identify anatomically relevant brain areas implicated in disease pathology. Challenges in terms of clinical uptake, integration into workflow, and standardized evaluation of explainability techniques are also discussed. The review identifies how the pairing of high-performing AI models with strong explainability methods has the potential to enable the creation of practical and reliable diagnostic tools for real-world clinical application.

Abstract:
Cardiovascular diseases are the most common diseases around the world and result in high morbidity and mortality rates. It proves the need to develop new approaches to the disease’s early diagnosis and prevention. Portable health monitoring is characterized by state-of-the-art sensors. It produces a continuous flow of physiological data in real-time. It presents a great opportunity for preventative Cardiovascular disease management. Using deep learning algorithms that is used to accurately and effectively. It creates preventive early diagnosing models for diseases. The authors discuss the integration of wearable health technology and deep learning towards the improvement of individual-oriented technologies in cardiology. The collected data are cleaned for noise and further normalized to provide uniform input in deep learning models. To detect anomalies that are likely to lead to cardiovascular risks, state-of-the-art algorithms. The models are trained and validated using a dataset of wearable device data and clinically diagnosed Cardiovascular disease cases. Wearable health devices combined with deep learning algorithms provide an innovative platform for cardiovascular disease screening and prevention. The incorporation of the proposed system proved its efficiency and accuracy in the identification of potential probable Cardiovascular disease risk, making it possible for early, real-time, noninvasive, and personalized healthcare services. This approach improves early detection but also provides handy information to the patients. This type of research includes the acquisition of larger databases and better models, and incorporating those into more offices and healthcare practices.

Emergency response systems are crucial for timely medical intervention, yet traditional methods suffer from delays, inefficient resource use, and a lack of real-time patient monitoring. Manual reporting and slow decisionmaking often lead to preventable fatalities. While machine learning and AI-based models improve emergency detection, they still struggle with real-time data integration, scalability, and predictive accuracy. This study proposes an IoT-driven emergency response system that combines real-time health monitoring with predictive analytics for early risk detection and optimized resource allocation. Unlike existing approaches, our method integrates IoT sensor data with AI-driven anomaly detection and time-series forecasting, ensuring continuous monitoring and proactive emergency management. Experimental evaluation shows 92% accuracy in health event detection, 85% improvement in response time, 95% system uptime, and 90% resource allocation efficiency, outperforming traditional and AI-based systems. Compared to rule-based and deep learning models, our system offers superior real-time reliability and proactive intervention capabilities. By enabling faster, more precise emergency detection and resource distribution, this approach significantly enhances patient survival rates and healthcare efficiency. Future enhancements will incorporate blockchain for secure data transmission, advanced AI for multi-disease prediction, and large-scale deployment to improve real-world applicability.

Background Information: The management of chronic diseases, fall prevention, and proactive healthcare are essential for enhancing care for the ageing population. Artificial intelligence (AI) and machine learning (ML) provide sophisticated instruments for predictive modelling, facilitating prompt interventions and tailored treatment approaches in geriatric care. Objectives: The objective of this work is to create predictive models utilising artificial intelligence and machine learning for the management of chronic diseases, fall detection, and preventative healthcare applications, hence improving care quality and patient outcomes in elderly populations. Methods: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN) models were trained using clinical and sensor data, both individually and in ensemble combinations, to forecast health risks. Results: The ensemble model attained 92% accuracy, 90% precision, 89% recall, 90% F1score, and 91% AUC-ROC, indicating superior predictive performance across all criteria. Conclusion: In conclusion, ensemble-based AI models improve risk prediction for the aged, facilitating proactive treatments and enhancing healthcare outcomes for senior patients.

The introduction of Artificial Intelligence (AI) into the healthcare industry holds immense promise for improving patient outcomes. However, the interaction between healthcare professionals and AI systems is critical to fully realize the potential benefits. Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of Al in healthcare and focuses on the following key aspects: medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation and other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using Ai-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.

This study explores the ethical challenges of AI-driven hiring, focusing on algorithmic bias, fairness, and transparency in recruitment. While AI hiring tools enhance efficiency, concerns persist about bias replication, lack of interpretability, and regulatory compliance. This paper reviews AI-driven hiring ethics through case studies and regulatory analysis, assessing the risks of automation in decision-making. The study highlights fairness-aware AI models, adversarial debiasing techniques, and hybrid AI
human hiring frameworks as potential solutions. Findings suggest that AI can reduce bias when integrated with explainable AI (XAI) and continuous fairness audits. Future research should examine industry-specific AI hiring ethics and long-term workforce diversity outcomes in AI-driven recruitment.

Chronic diseases such as diabetes, hypertension, heart disease, and chronic obstructive pulmonary disease (COPD) are prevalent worldwide, demanding innovative solutions for efficient management. Managing chronic diseases typically requires continuous monitoring, medication adjustments, and frequent visits to healthcare providers. Traditional in-person care, however, can be burdensome for both patients and healthcare systems. Telemedicine and remote patient monitoring (RPM) have revolutionized healthcare by offering patients the ability to receive continuous, personalized care outside of traditional clinical settings. Artificial Intelligence (AI) has emerged as a key technology, enhancing telemedicine and RPM in chronic disease management. The integration of AI into these technologies holds promise for enhancing disease management by offering real-time analytics, predictive capabilities, and personalized interventions. This systematic review seeks to provide an in-depth examination of the role of AI in telemedicine and RPM for chronic disease management, exploring its potential, challenges, and implications for future healthcare delivery. This paper presents a systematic review of the role of AI in telemedicine and RPM for chronic disease management, providing insights into its benefits, challenges, and future trends.

The rapid proliferation of botnets, armies of compromised machines controlled by malicious actors remotely, has played a pivotal role in the increase in cyber-attacks, such as Distributed Denial-of-Service (DDoS) attacks, credential theft, data exfiltration, command-and-control (C2) activity, and automated exploitation of vulnerabilities. Legacy botnet detection methods, founded on signature matching and deep packet inspection (DPI), are rapidly becoming a relic of the past because of the prevalence of encryption schemes like TLS 1.3, DNS-over-HTTPS (DoH), and encrypted VPN tunneling. These encryption mechanisms conceal packet payloads, making traditional network monitoring technology unsuitable for botnet detection. Faced with the challenge, ML-based botnet detection mechanisms have risen to the top. Existing ML-based approaches, however, are marred by two inherent weaknesses: (1) Lack of granularity in detection because most models are based on binary classification, with no distinction of botnet attack variants, and (2) Uninterpretability, where high-performing AI models behave like black-box mechanisms, which limits trust in security automation and leads to high false positives, thereby making threat analysis difficult for security practitioners. To overcome these challenges, this study proposes an AI-based, multi-class classification botnet detection system for encrypted network traffic that includes Explainable AI (XAI) techniques for improving model explainability and decision transparency. Two datasets, CICIDS-2017 and CTU-NCC, are used in this study, where a systematic data preprocessing step was employed to maximise data quality, feature representation, and model performance. Preprocessing included duplicate record removal, missing and infinite value imputation, categorical feature transformation, and removal of highly correlated and zerovariance features to minimise model bias. Dimensionality reduction was performed using Principal Component Analysis (PCA), lowering features of CICIDS-2017 from 70 to 34 and those of CTU-NCC from 17 to 4 for maximizing computational efficiency. Additionally, to deal with skewed class distributions, Synthetic Minority Over-Sampling Technique (SMOTE) was employed to synthesise minority class samples to offer balanced representation of botnet attack types. For CICIDS-2017, we used three machine learning algorithms: Random Forest (RF) with cross-validation (0.98 accuracy, 100K samples per class), eXtreme Gradient Boosting (XGB) with Bayesian optimisation (0.997 accuracy, 180K samples per class), and our recently introduced Hybrid K-Nearest Neighbours(KNN) + Random Forest (RF) model, resulting in state-of-the-art accuracy of 0.99 (180K samples per class). The CTU-NCC dataset was divided across three network sensors and processed separately. Random Forest (RF), Decision Tree (DT), and KNN models were trained independently for each sensor, and to enhance performance, ensemble learning methods such as stacking and voting were applied to combine the results from each of the sensors. The resulting accuracies were as follows: (Random Forest Stacking: 99.38%, Random Forest Voting: 99.35% ), (Decision Tree Stacking: 99.68%, DecisionTree Voting: 91.65%), and (KNN Stacking: 97.53%, KNN Voting: 97.11%). Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model agnostic Explanation) were integrated to provide enhanced interpretability in eXtreme Gradient Boosting and our Hybrid KNN+Random Forest model, which provided explanations for model decisions and enhanced analyst confidence in the system prediction.
Our key contribution is the Hybrid KNN+Random Forest system with 0.99 accuracy and provision of explainability. We illustrate an accurate, scalable, and deployable AI-based solution for botnet attacks. Our experimentation shows that the multi-class classification method greatly assists in botnet attack discrimination, and Explainable AI (XAI) helps enhance clarity and is thus a strong, practical solution in the real case of botnet detection in an encrypted network scenario.

Artificial intelligence (AI) possesses the capacity to transform numerous facets of our existence; however, it concomitantly engenders considerable risks associated with bias and discrimination. This article explores emerging technologies like Explainable AI (XAI), Fairness Metrics (FMs), and Adversarial Learning (AL) for bias mitigation while emphasizing the critical role of transparency, accountability, and continuous monitoring and evaluation in AI governance. The Holistic AI Governance Framework (HAGF) is introduced, featuring a comprehensive, five-layered structure that integrates top-down and bottom-up strategies. HAGF prioritizes foundational principles and resource allocation, outlining five lifecycle-specific phases. Unlike the OECD AI Principles, which offer a general ethical framework lacking holistic perspective and resource allocation guidance, and the Berkman Klein Center's Model, which provides a broad framework but omits resource allocation and detailed implementation, HAGF offers actionable mechanisms. Tailored Key Performance Indicators (KPIs) are proposed for each HAGF layer, enabling ongoing refinement and adaptation to the evolving AI landscape. While acknowledging the need for enhancements in data governance and enforcement, the embedded KPIs ensure accountability and transparency, positioning HAGF as a pivotal framework for navigating the complexities of ethical AI.

The healthcare industry is under increasing pressure to improve patient outcomes while managing rising operational costs. Healthcare professionals need consistent inputs, insights, recommendations, alerts, and data evidence to enhance the quality of care, make clinical decisions, and reduce errors. A Clinical Decision Support System (CDSS) is an interactive software designed to assist healthcare providers in making clinical decisions. Artificial Intelligence (AI) has emerged as a promising tool to improve clinical decision-making, offering the potential to reduce costs, optimize workflows, and improve care delivery. AI technologies, such as machine learning, natural language processing, and predictive analytics, are increasingly being integrated into CDSS, enabling healthcare providers to make more accurate, timely, and evidence-based decisions. This paper explores how the implementation of AI in clinical decisionmaking affects operational costs and patient outcomes. It examines both the positive impacts and potential challenges of AI integration, focusing on its role in improving clinical efficiency, enhancing diagnostic accuracy, reducing errors, and ultimately transforming healthcare delivery.

A class imbalance occurs when there is a significant difference among the two categories of the target variable, with numerous occurrences of one class and few instances of the other. This issue has grown more common in many domains that use models for forecasting, such as illness forecasting techniques, which use information mining and machine learning to solve issues in the healthcare industry. Since the method attends to acquire additional about the larger class owing to its a large sample and acknowledge fewer of the minority class in response to its reduced quantity, class disparity specifically triggers an algorithm powered by machine learning to incorrectly identify events from the minority group even though it can accomplish high precision. This happens since the method might just categorize every case as the a majority class in an information set in class disparity. Public confidence in the consequences of the choice based on the prejudiced result is also impacted by this problem, in addition to the algorithm's forecast outcomes. If this issue of class imbalance is not resolved, the predictive model may label the minority class samples incorrectly, which might undermine the validity of the model's findings. This article offers a thorough analysis of methods for fixing data imbalance and the difficulties associated with doing so.

Predictive health analytics, empowered by Artificial Intelligence (AI), has revolutionized healthcare by enhancing disease prediction, diagnosis, and treatment. This literature review delves into the integration of AI in predictive health analytics, exploring various AI techniques, applications, and their impact on healthcare outcomes. The review identifies key themes, including data mining, machine learning (ML) algorithms, Electronic Health Records (EHR), and predictive modeling. Data mining techniques, such as clustering and association rule mining, help extract meaningful patterns and relationships from vast amounts of healthcare data. ML algorithms, including decision trees, random forests, and neural networks, enable the development of predictive models that can forecast disease risk, progression, and treatment outcomes. EHRs provide a comprehensive repository of patient data, facilitating the application of AI in predictive health analytics. Predictive modeling techniques leverage AI algorithms to analyze healthcare data and generate predictions about future health events, enabling proactive interventions and personalized care. This review showcases the potential of AI to revolutionize healthcare by providing personalized, efficient, and accurate predictive analytics, ultimately leading to improved patient outcomes and a more sustainable healthcare system.

The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.

Chronic diseases represent a significant burden on global health systems, accounting for a substantial proportion of morbidity and mortality worldwide. The early diagnosis of these diseases is crucial for effective management and improved patient outcomes. Recent advancements in machine learning (ML) offer promising avenues for enhancing diagnostic accuracy and efficiency. This paper explores the opportunities presented by machine learning in the early diagnosis of chronic diseases, including its potential to analyze complex datasets, improve predictive accuracy, and facilitate personalized medicine. However, the integration of machine learning into clinical practice also poses several challenges, such as data privacy concerns, algorithmic bias, and the need for robust validation. By examining both the opportunities and challenges, this paper aims to provide a comprehensive overview of the role of machine learning in the early diagnosis of chronic diseases and to highlight future directions for research and implementation.

As the global population ages, the prevalence of cognitive decline and dementia, including Alzheimer's disease, continues to rise, impacting millions of individuals and placing a significant burden on healthcare systems. Early prediction and accurate monitoring of dementia progression are critical for timely intervention, personalized care, and slowing disease advancement. However, traditional diagnostic approaches face challenges, such as reliance on late-stage biomarkers, limited sensitivity of cognitive assessments, and inconsistencies in neuroimaging. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming the field of dementia prediction, offering a paradigm shift toward earlier and more accurate assessments. This paper systematically examines recent advancements in AI and ML applications in predicting cognitive decline and tracking dementia progression. Key technologies discussed include deep learning for neuroimaging analysis, natural language processing (NLP) for speech and language pattern identification, and time-series analysis for continuous monitoring through wearable devices. The role of multimodal data integration, encompassing genetic, behavioral, clinical, and imaging data, is highlighted as a critical advancement that AI can facilitate, allowing for a comprehensive and personalized approach to risk prediction. Despite AI's potential, significant challenges remain, including data quality and diversity, ethical concerns in predictive diagnostics, and the "black-box" nature of many AI models that make clinical interpretability difficult. The review also discusses the regulatory and ethical landscape, underscoring the need for transparent, unbiased, and privacy-conscious AI applications in healthcare. Future directions are proposed, such as advancements in explainable AI (XAI), integration of precision medicine approaches, and the role of AI in supporting drug development and clinical trials. In conclusion, while AI and ML offer promising tools for enhancing dementia prediction and management, a collaborative approach involving researchers, clinicians, policymakers, and patients is essential to harness AI's potential responsibly and equitably. This paper calls for continued research, interdisciplinary partnerships, and regulatory guidance to ensure AI's ethical and effective integration into dementia care and management.

This paper explores the ethical implications of AI-driven decision-making in cloud-based services, highlighting key concerns such as algorithmic bias, data privacy, accountability, and transparency. As AI systems become increasingly embedded in cloud infrastructures, they raise significant ethical challenges that must be addressed to ensure responsible and fair deployment. The paper examines emerging trends, including explainable AI (XAI), automated fairness tools, and the importance of data governance, which offer solutions to mitigate these challenges. However, it also identifies ongoing issues such as bias in AI models, inadequate data privacy protection, cybersecurity vulnerabilities, and the lack of a consistent global regulatory framework. A survey of industry stakeholders reveals that data privacy and algorithmic bias are perceived as the most pressing challenges. The paper also discusses strategies to overcome these issues, such as fostering collaboration across stakeholders, investing in continuous monitoring, and developing global ethical standards for AI systems. Ultimately, the paper calls for a multi-faceted approach combining innovation, regulation, and accountability to ensure that AI-driven decision-making in cloud-based services is ethical, transparent, and beneficial for all stakeholders.

This paper explores the ethical implications of AI-driven decision-making in cloud-based services, highlighting key concerns such as algorithmic bias, data privacy, accountability, and transparency. As AI systems become increasingly embedded in cloud infrastructures, they raise significant ethical challenges that must be addressed to ensure responsible and fair deployment. The paper examines emerging trends, including explainable AI (XAI), automated fairness tools, and the importance of data governance, which offer solutions to mitigate these challenges. However, it also identifies ongoing issues such as bias in AI models, inadequate data privacy protection, cybersecurity vulnerabilities, and the lack of a consistent global regulatory framework. A survey of industry stakeholders reveals that data privacy and algorithmic bias are perceived as the most pressing challenges. The paper also discusses strategies to overcome these issues, such as fostering collaboration across stakeholders, investing in continuous monitoring, and developing global ethical standards for AI systems. Ultimately, the paper calls for a multi-faceted approach combining innovation, regulation, and accountability to ensure that AI-driven decision-making in cloud-based services is ethical, transparent, and beneficial for all stakeholders.

As the global population ages, the demand for long-term care (LTC) services has risen significantly. Long-term care facilities that support residents with chronic illnesses, disabilities, or age-related conditions face numerous challenges in delivering effective and efficient care. Traditional methods of monitoring residents' health often rely on periodic check-ups, which may overlook subtle changes in health, contributing to delayed interventions and escalating healthcare costs. AI-enabled remote health monitoring represents a transformative solution to these challenges. By leveraging artificial intelligence (AI) technologies, such as wearable devices, smart sensors, and predictive analytics, healthcare providers can continuously monitor vital signs, detect health issues early, and personalize care plans for each resident. This white paper explores the current landscape of AI-driven monitoring in LTC, its benefits, challenges, and future potential.

Artificial Intelligence (AI) and Machine Learning (ML) can potentially revolutionise healthcare systems by improving diagnostic and treatment procedures, thereby enhancing patients' health. Based on big data, these technologies can find correlations that human eyes cannot see. Doing so may lead to earlier diagnoses, better treatments, and more effective drug development. However, the use of AI and ML in the healthcare sector cannot succeed without addressing issues of ethics and professionalism, such as data privacy and inductive bias. To protect patients, it is important that these technologies are designed and used ethically, ensuring that patients have control over their deployment. With the continual development of AI and ML technologies, it is necessary to invest more in research and development to tackle their potential issues.

Smart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaking advancement in continuous health monitoring and disease detection. This paper explores the development and application of smart contact lenses that integrate real-time data collection with sophisticated AI algorithms to monitor various health parameters. By incorporating sensors and AI processing units directly into the lenses, these devices can continuously measure and analyze biometric data such as glucose levels, intraocular pressure, and ocular health indicators. The embedded AI system processes this data to provide real-time feedback, identify potential health issues, and predict disease onset with high accuracy. The study evaluates the efficacy of these smart contact lenses in detecting early signs of chronic conditions like diabetes and glaucoma, and their ability to enhance patient engagement through personalized health insights. Additionally, the paper discusses the technological challenges, including data accuracy, device comfort, and privacy concerns, and proposes solutions to address these issues. The integration of smart contact lenses with AI technology offers a novel approach to proactive health management, paving the way for more accessible and effective continuous monitoring and early disease detection.

The United States faces significant challenges in long-term care (LTC) due to the aging population, which strains healthcare systems and contributes to rising costs. Simultaneously, the economic implications of an aging population require urgent solutions that not only address healthcare sustainability but also have the potential to stimulate economic growth. Artificial Intelligence (AI) can significantly reduce LTC costs by improving efficiency, quality of care, and resource allocation. Additionally, AI-driven innovations can generate economic benefits, such as job creation in the Technology sector, better workforce productivity, and cost savings that can be reinvested into other parts of the economy. This paper explores how AI can address the economic challenges LTC poses and offers a pathway to boosting the US economy.

This paper examines and identifies the difficulties and probable solutions for leveraging the implementation of Electronic Health Records with the RXclaims system. Therefore, based on the analysis of technical dissimilarities, data standardisation problems, and difficulties in interaction between two crucial systems, the purpose is to bring two systems together. We examine standards and protocols related to Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) and use case studies and practical experience to refine the integration. Consequently, this research aims at improving patient outcomes, reducing healthcare costs, and increasing the efficiency of health care delivery resulting from interoperability between Electronic Health Records (EHRs) and insurance systems.

This article provides an in-depth analysis of the use of Artificial Intelligence (AI) in various aspects of biology, including healthcare, agriculture, and environmental monitoring. It highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks. The article also discusses its integration into Chinese medicine, where AI-guided diagnostic and therapeutic systems optimize clinical treatments and health management. AI is also used in disease management, analyzing data on diseases and pests, predicting their impact on ecosystems, and implementing preventative measures. The article also highlights the role of integrated information systems in environmental monitoring. Artificial intelligence (AI) has significant potential in healthcare research and chemical discoveries. Pharmaceutical companies are using AI to improve drug development by utilizing computational biology and machine learning systems to predict molecular behavior and the likelihood of finding a useful drug. This saves time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources for drug development. Strong AI systems can analyze extensive data sets in pharmaceutical and medical research. This review focuses on integrating knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in cancer precision drug discovery.

In the transformative realm of healthcare, Artificial Intelligence (AI) stands out as a beacon of hope for drastically improving patient outcomes. This narrative explores the strategic deployment of AI to enhance diagnostics, personalize treatments, and predict medical events before they unfold. Overcoming challenges such as data privacy, ethical considerations, and the need for substantial infrastructure is crucial for success. AI's promise extends to revolutionizing preventive care, streamlining healthcare operations, and enabling medical professionals to concentrate on providing care. By fostering cross-disciplinary collaborations and advocating for ethical AI practices, the healthcare sector can unlock AI's full potential, paving the way for a future where healthcare is not only reactive but also predictive and personalized, ensuring improved health outcomes for all.

Rapid advances in the development of machine learning algorithms provide an opportunity to revolutionize treatment decisions at the end of life, particularly for patients who are unable to communicate their own wishes. However, current legal regulation jars with this increased potential and often precludes vital steps from being practically taken in the clinical sphere. The medical sphere is further confined by current incomplete ethical and legal guidance on how predictive algorithms should be deployed in the hospital setting in general. After an analysis of the application of machine learning and deep learning to end-of-life outcomes, our paper outlines 1 how machine learning can be used for principled end-of-life decision-making, tightly drawing the implications of the proposed solutions from pertinent ethical theory and case law. Historically, the prediction of terminal prognostic outcomes and the formulation of potential competing legal, ethical, and clinical responses to these predictions have taken place in parallel. On the one hand, clinicians have been equipped with a range of prognostic tools to aid estimates of life expectancy for patients approaching the end of their lives. On the other, legal guidance has been developed to codify the timing at which life support can properly be withheld or withdrawn from terminally unwell patients. Nowadays, predictive algorithmic advances have the capacity to revolutionize individualized terminal prognosis. Data analysis significantly improves palliative care outcomes and can ensure that patientswho are traditionally vulnerable to clinicians' well-intentioned overoptimismcan be provided with transparent, personalized information about the end of their lives. With clearer knowledge, the decision to forgo aggressive therapy and move towards a palliative care approach can better correspond to a rational patient's end-of-life preferences.

Rapid advances in the development of machine learning algorithms provide an opportunity to revolutionize treatment decisions at the end of life, particularly for patients who are unable to communicate their own wishes. However, current legal regulation jars with this increased potential and often precludes vital steps from being practically taken in the clinical sphere. The medical sphere is further confined by current incomplete ethical and legal guidance on how predictive algorithms should be deployed in the hospital setting in general. After an analysis of the application of machine learning and deep learning to end-of-life outcomes, our paper outlines 1 how machine learning can be used for principled end-of-life decision-making, tightly drawing the implications of the proposed solutions from pertinent ethical theory and case law. Historically, the prediction of terminal prognostic outcomes and the formulation of potential competing legal, ethical, and clinical responses to these predictions have taken place in parallel. On the one hand, clinicians have been equipped with a range of prognostic tools to aid estimates of life expectancy for patients approaching the end of their lives. On the other, legal guidance has been developed to codify the timing at which life support can properly be withheld or withdrawn from terminally unwell patients. Nowadays, predictive algorithmic advances have the capacity to revolutionize individualized terminal prognosis. Data analysis significantly improves palliative care outcomes and can ensure that patientswho are traditionally vulnerable to clinicians' well-intentioned overoptimismcan be provided with transparent, personalized information about the end of their lives. With clearer knowledge, the decision to forgo aggressive therapy and move towards a palliative care approach can better correspond to a rational patient's end-of-life preferences.

This paper explores the potential impact of Explainable Artificial Intelligence (XAI) on long-term care (LTC), highlighting the value of transparent and interpretable AI in supporting clinical decision-making, enhancing patient trust, and improving the quality of care. With an aging global population, LTC facilities are increasingly adopting AI tools to manage patient health and operational efficiency. XAI can play a critical role by ensuring that predictions and recommendations are understandable and actionable for caregivers and clinicians, fostering trust, accountability, and ethical use of AI in patient care.

The BOOK "Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare" explores the powerful synergy between Data Envelopment Analysis (DEA) and Machine Learning (ML) to drive performance optimization in the healthcare sector. DEA is traditionally used to evaluate the efficiency of healthcare providers by comparing inputs and outputs. However, its deterministic nature often falls short in capturing the complexities of modern healthcare operations. On the other hand, ML offers advanced capabilities to analyze high-dimensional data and uncover intricate patterns that can lead to more accurate predictions and insights. This book delves into the integration of these two methodologies, highlighting how their combined strengths can overcome the limitations of each and result in more effective performance optimization. With the exploration of theoretical foundations, methodological innovations, and real-world applications, the book aims to provide a comprehensive resource for researchers, practitioners, and policymakers. Healthcare systems globally face increasing pressures to improve efficiency, manage resources better, and deliver higher quality care. Traditional approaches often struggle to meet these demands, but the DEA-ML synergy offers a promising solution. The book will showcase how this integration can enhance efficiency assessments, improve resource allocation, and optimize clinical pathways, leading to better patient outcomes and more effective healthcare management. Through a blend of academic rigor and practical case studies, the book seeks to revolutionize the field of healthcare performance optimization, making it a must-read for anyone involved in healthcare management, operations research, data science, and artificial intelligence.

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient
and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making,
precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI’s role in public health during the
early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent,
accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy
information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic
approaches to harness AI’s potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and
integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological
literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.