Optimization Techniques for Asthma Exacerbation Prediction Models: A Systematic Literature Review

Asthma exacerbations pose a significant global health concern, necessitating effective predictive models to anticipate and manage these events. This systematic literature review examined the optimization techniques employed in asthma exacerbation prediction models, spanning machine learning algorithms and computational optimization methods. The objective was to synthesize existing evidence, identify trends, and delineate future research directions in predictive modeling for asthma exacerbations to enhance predictive accuracy and clinical utility. A comprehensive search strategy was devised, yielding 27 eligible articles for analysis. The result revealed various optimization techniques, including feature selection, model optimization, and environmental factor integration. The result also revealed that machine learning algorithms’ effectiveness in predicting asthma exacerbations varied depending on various factors (such as dataset quality and model complexity), with various optimization techniques (such as feature selection and ensemble learning) used for improving predictive accuracy. Integrating environmental and spatial factors enhanced prediction models, enabling tailored interventions. In addition, personalized asthma management strategies informed by predictive models led to better control and reduced healthcare utilization. The review also highlighted the implications for personalized asthma management, as well as methodological limitations, and proposed future research directions to improve model reliability and advance personalized healthcare understanding, thereby contributing to the United Nations’ Sustainable Development Goals related to health, innovation, and sustainability. Thus, progress made in asthma exacerbation prediction and the identification of challenges and areas for improvement were covered, providing valuable insights for researchers, clinicians, and policymakers aiming to enhance asthma care through predictive modeling.