Heatstroke is an increasingly critical public health concern, intensified by rising global temperatures and the growing frequency of extreme heat events. This study addresses the urgent need for timely and accurate heatstroke risk prediction by leveraging machine learning techniques. The primary objective is to develop a predictive model capable of identifying individuals at risk based on environmental and physiological data. An extensive dataset of 81,215 instances and 69 features underwent thorough preprocessing and analysis. Four machine learning algorithms - decision tree, random forest, logistic regression, and light gradient boosting machine (LightGBM) - were implemented and evaluated. Among these, LightGBM achieved the highest accuracy of 99.93%, demonstrating superior predictive performance and generalisation capability, as validated through confusion matrices and training-validation accuracy curves. Feature selection played a crucial role in optimising model effectiveness. The findings underscore the potential of machine learning as a valuable tool in predictive healthcare. Future work will focus on integrating real-time sensor data, enabling personalised risk assessments, and deploying a mobile-based alert system to enhance heatstroke prevention. This research contributes to proactive public health strategies through an AI-driven framework for early detection and intervention.
Haque et al. (Thu,) studied this question.