Abstract:

Weather-Based Healthcare (WBH) manages the knowledge of chronic diseases based on weather, such as asthma. Previous research shows that weather triggers such as humidity and temperature, often lead to asthma exacerbation and acute asthma attacks. Nonetheless, specific weather impacts the lung performance of individual asthmatic patients differently, and are correlated to their demographic features (e.g. gender and age). As such, personalisation is significant to recognise the potential weather attributes that can trigger each individual patient’s asthma. ln this case, Machine Learning (ML) techniques are helpful in developing an intelligent system for effective asthma knowledge management. In recent years, asthma knowledge management has seen multiple trials in building Mobile Health (mHealth) applications. This study identifies the ML potentials in correlation with weather, asthma, and demographic features, which interplay when predicting personalised asthma exacerbation influenced by weather effects. This work would help to integrate the optimised predictive model into mHealth solutions to build intelligent systems for effective asthma knowledge management and WBH.