ABSTRACT
The fundamental element of people's needs is health. Humans face a haul of surprising death and plenty of diseases because of varied diseases resulting from a lack of treatment to patients at the right time. By seamlessly integrating the Internet of Things (IoT), Machine Learning, and Shapley Additive Explanations (SHAP), this study presents a revolutionary paradigm for health monitoring. The system collects real-time health data via IoT devices. Machine learning approaches are used for comprehensive health pattern analysis, and SHAP values aid in assessing interpretability and feature importance. The Random Forest model achieved an excellent accuracy of 60% in the experiments, demonstrating a substantial breakthrough. This study demonstrates the efficacy of combining IoT with machine learning, highlighting its potential for developing health monitoring systems. The method not only produces accurate forecasts, but it also provides essential insights into the critical elements impacting health outcomes, hence helping to the growth of tailored and data-driven healthcare solutions.
