Machine Learning Enabled IoT Framework for Early Health Risk Prediction Using Wearable Sensor Data

Abstract

This research presents a novel machine learning Internet of Things (IoT) framework designed for the early prediction of health risks utilizing wearable sensor data. Employed a hybrid methodology combining data collection from diverse wearable devices, preprocessing to ensure high-quality input, and advanced machine learning algorithms for risk assessment. The framework integrates real-time data analytics with predictive modeling, allowing for the continuous monitoring of health metrics such as heart rate variability, physical activity levels, and sleep patterns. Our findings reveal that the implemented model accurately predicts potential health risks, achieving a classification accuracy of over 90% across multiple test cases involving various demographics. The model also demonstrated robustness in handling noise and incomplete data, highlighting its applicability in real-world settings. The significance of this research lies in its potential to transform preventive healthcare by enabling timely interventions, thereby mitigating severe health conditions before they develop. This framework lays the groundwork for further exploration in automated health monitoring systems and paves the way for personalized healthcare strategies through the integration of IoT technologies, ultimately contributing to enhanced health outcomes and improved quality of life for users.