Developing Enhanced Asthma Severity Predictive Model using Random Forest Algorithm

Abstract

Asthma is a chronic respiratory condition shaped by environmental, physiological, and behavioral factors. Accurate prediction of asthma severity is vital for personalized care and reducing exacerbations. While Machine Learning (ML) has been widely explored in asthma prediction, many existing models lack generalizability, robustness, or comprehensive integration of multiple predictors, limiting their clinical applicability. This study presents a robust ML-based model for classifying asthma severity by incorporating diverse patient and environmental features. A supervised learning approach was employed using a publicly available dataset of 1,010 records with 14 features, including demographics, clinical symptoms, and environmental indicators (temperature, wind speed, and humidity). The dataset was pre-processed and stratified to balance severity classes. Three models—Decision Tree, Support Vector Machine (SVM), and Random Forest (RF) were evaluated using standard metrics: accuracy, precision, recall, F1-score, and ROC AUC. Among them, the RF model showed superior performance, achieving 96.70% accuracy, a 0.9668 F1-score, and a 0.9831 ROC AUC. Feature importance analysis highlighted environmental factors, particularly temperature and humidity, as key predictors of asthma severity. These results underscore RF's effectiveness in providing accurate, interpretable predictions and addressing limitations of earlier models. The proposed model offers a data-driven framework for real-time severity forecasting, supporting early interventions and personalized treatment. It holds promise for integration into clinical decision support systems, thereby enhancing asthma management and optimizing healthcare resource use. It is therefore recommended that the proposed model be operationalized within clinical triage protocols, embedded into electronic health record (EHR) infrastructures, or integrated into mobile health (mHealth) applications to facilitate data-driven, proactive asthma management across diverse care settings in Nigeria.

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