Integrative IoT Solution for Heatstroke: Machine Learning Enhancement and Healthcare Application

Heatstroke, a grave human health issue exacerbated by global warming, results from the body's overheating due to prolonged exposure or physical exertion in high temperatures. Addressing this escalating concern, this paper proposes a self-aware heatstroke early-warning system leveraging the healthcare Internet of Things (IoT) and sophisticated machine learning techniques. The enhanced system, designed to be accessible and affordable for the middle class, incorporates a NodeMCU-based IoT prototype equipped with temperature and pulse sensors, providing comprehensive real-time monitoring. For data analysis, this study employs a suite of machine learning algorithms, including decision trees, AdaBoost, Gaussian Naive Bayes, linear regression, and neural networks, implemented in Python Jupyter Notebook. The performance of these models is rigorously evaluated using accuracy and confusion performance matrices to ensure reliable, personalized, and timely heatstroke predictions. By integrating advanced analytics and IoT technologies, the proposed system aims to enhance accuracy, enable early detection, and tailor responses to individual physiological conditions, significantly contributing to preventive healthcare measures against heatstroke.