DEVELOPMENT OF ADVANCED WEARABLE HEALTH DEVICES FOR REAL-TIME, CONTINUOUS MONITORING AND EARLY DETECTION OF CHRONIC DISEASES USING INTEGRATED BIOSENSORS AND MACHINE LEARNING

Abstract

The increasing prevalence of chronic diseases has created a pressing need for innovative solutions to enable early detection and continuous monitoring. This study presents the development of an advanced wearable health device integrated with biosensors and machine learning algorithms for real-time monitoring and early detection of chronic diseases. The device was designed to measure critical biomarkers, including heart rate, blood glucose, oxygen saturation, and blood pressure, providing continuous, non-invasive monitoring. The device demonstrated high accuracy across these sensors, with the heart rate sensor achieving 98.2% accuracy and the blood glucose sensor reaching 95.1% accuracy.The simulation utilized machine learning models to forecast diseases where the random forest model achieved the highest performance rate of 93.1%.  The system demonstrated strong connections to real medical diagnoses for cardiovascular problems and diabetes and respiratory problems during testing that reached a 91.5% accuracy level while verifying chronic illness indicators from clinical databases.  Users from multiple age brackets show signs of adopting this gadget because it receives positive feedback about its easy operation and comfort features.  Wearable sensors when combined with machine learning technology demonstrate their capability to generate individual disease information while offering early diagnosis which helps resolve important chronic disease treatment hurdles.  Through continuous monitoring activities enabled by wearable technology human healthcare is revolutionizing because it enables enhanced predictive skills along with better patient outcomes.  Future public health benefits from the device rely on enhancing sensor incorporation and machine learning algorithms while handling privacy issues and sensor integration.