Asthma Management

Asthma is a respiratory disease that affects millions of people and has become one of the
major causes of death worldwide. Early predictions of asthma can help health workers take the
necessary precautions to prevent further complications. The traditional ways of asthma prediction
are no longer effective because they are prone to error and time-consuming. Studies have employed
sophisticated techniques, such as machine learning and deep learning, for asthma prediction, yielding
promising results. However, previous research failed to consider datasets from different
demographics, limiting the models to a particular population. Also, previous research has failed to
perform a thorough comparative analysis between ensemble, machine learning, and deep learning
models. This research addresses these gaps by developing a comparative multi-source predictive
framework using four different algorithms, including Random Forest (RF), Support Vector Machine
(SVM), Multi-Layer Perceptron, and a hybrid Stacking Ensemble model (SVM + RF + MLP), using
datasets collected from Federal Teaching Hospital Lokoja, Specialist Hospital Lokoja, and Kaggle
data. The dataset undergoes the process of an 80/20 stratified train/test split, removing of low
variance using a threshold of 0.01. The training set was balanced using SMOTETomek. The three
models (RF, SVM, MLP) were tuned using GridSearchCV hyperparameter optimization (with 5-fold
cross-validation) before combining the best-performing models in a stacking ensemble (SVM + RF
+ MLPF) with a LogisticRegression meta-learner, leveraging kernel-based, tree-based, and neural
network models for improved predictive performance. The models' performances were compared
using Accuracy, Recall, F1-Score, and AUC-ROC. The hybrid stacking ensemble model achieved
the highest AUC-ROC (0.9910) while maintaining 99.23% accuracy and an F1-score of 0.9920. RF
and the ensemble models identified the most important predictors, variables that distinguish between
asthmatic and non-asthmatic patients. The study demonstrates that integrating heterogeneous
datasets improves predictive robustness and provides a strong foundation for real-world asthma
detection systems.

Asthma is one of the most common chronic respiratory diseases in childhood and is characterized by chronic airway inflammation, bronchial hyperresponsiveness, and variable airflow obstruction. It causes recurrent episodes of wheezing, cough, shortness of breath, and chest tightness that may vary in frequency and severity.

This educational lecture presentation explains the pathophysiology, clinical features, triggers, diagnostic clues, and danger signs of asthma in children in a simple and clinically oriented manner.

Topics covered include:

Asthma in children
Chronic airway inflammation
Bronchial hyperresponsiveness
Reversible airflow obstruction
Th2-mediated immune response
IgE-mediated allergy
Eosinophilic inflammation
Viral upper respiratory infections
Exercise-induced symptoms
Bronchospasm
Airway edema
Mucus production
Wheezing
Nocturnal cough
Dyspnea
Asthma diagnosis
Atopy and family history
Severe asthma warning signs
Respiratory distress in children

This presentation is useful for:

MBBS students
FCPS Pediatrics aspirants
MD Pediatrics trainees
Nursing students
Medical educators
Parents and caregivers
Healthcare professionals interested in pediatric respiratory diseases

Prepared for educational and revision purposes in pediatrics, pulmonology, immunology, and emergency medicine.

Watch detailed educational lectures on YouTube:
Novice Medic

Asthma among university students requires effective self-management supported by appropriate health information. However, there is a limited understanding of students' information needs and how they apply such information in daily life exists, particularly in Ghanaian university settings. This study explored the health information needs and self-management practices of students with asthma at the University of Health and Allied Sciences (UHAS). A qualitative descriptive design was employed. In-depth interviews were conducted with 20 students living with asthma across various academic programmes and years of study. Data were analyzed thematically in line with two objectives: 1) information needs regarding symptom management and trigger prevention, and 2) utilization of information in daily asthma self-management. Findings revealed diverse levels of asthma understanding, ranging from basic awareness to clinical knowledge. Participants reported strong needs for practical, simple, and actionable information, particularly for managing acute attacks and identifying hidden triggers such as stress, chemicals, and environmental exposures. Healthcare professionals were the most trusted information source, while digital platforms were widely used but inconsistently interpreted due to varying digital health literacy. Self-management practices ranged from structured and proactive behaviors to reactive symptom-based responses, influenced by emotional factors, academic stress, and environmental conditions. Confidence in managing asthma varied widely. Asthma self-management among students is shaped by gaps in practical knowledge, emotional burden, and contextual university factors. Strengthening tailored health education and digital health literacy is essential.

Asthma among university students requires effective self-management supported by appropriate health information. However, there is a limited understanding of students’ information needs and how they apply such information in daily life exists, particularly in Ghanaian university settings. This study explored the health information needs and self-management practices of students with asthma at the University of Health and Allied Sciences (UHAS). A qualitative descriptive design was employed. In-depth interviews were conducted with 20 students living with asthma across various academic programmes and years of study. Data were analyzed thematically in line with two objectives:
1) information needs regarding symptom management and trigger prevention, and
2) utilization of information in daily asthma self-management. Findings revealed diverse levels of asthma understanding, ranging from basic awareness to clinical knowledge. Participants reported strong needs for practical, simple, and actionable information, particularly for managing acute attacks and identifying hidden triggers such as stress, chemicals, and environmental exposures. Healthcare professionals were the most trusted information source, while digital platforms were widely used but inconsistently interpreted due to varying digital health literacy. Self-management practices ranged from structured and proactive behaviors to reactive symptom based responses, influenced by emotional factors, academic stress, and environmental conditions. Confidence in managing asthma varied widely. Asthma self-management among students is shaped by gaps in practical knowledge, emotional burden, and contextual university factors.

BackgroundEvidence suggests a causal relationship between obesity and asthma; however, the underlying mechanisms remain unknown. Substance P (SP), involved in neurogenic inflammation by acting through its receptor NK1‐R, seems to participate in obese–asthma phenotype in mice.ObjectivesTo evaluate the effect of a selective substance P receptor antagonist on a mouse model of diet‐induced obesity and asthma.MethodsDiet‐induced obese Balb/c mice were sensitized and challenged with ovalbumin (OVA) and treated with a selective NK1‐R antagonist or placebo. Serum glucose, insulin, IL‐6, resistin, and OVA‐specific IgE levels were quantified. A score for peribronchial inflammation in lung histology was used. Cells were counted in bronchoalveolar lavage fluid. Adipocyte sizes were measured.ResultsOvalbumin‐obese mice treated with NK1‐R antagonist had lower weight (P = 0.0002), reduced daily food intake (P = 0.0021), reduced daily energy intake (P = 0.0021), reduced surface adipocyte areas (P &...