Machine Learning-Driven Wearable IoT Health Monitoring

Wearable Internet of Things (IoT) devices have become a major enabler of continuous health monitoring, providing real-time data such as ECG, heart rate, temperature, and oxygen saturation for early disease detection. Integrating machine learning (ML) with wearable sensors enhances predictive accuracy and enables timely medical intervention. Conventional approaches such as Rule-Based Health Monitoring (RBHM), Threshold-Based Anomaly Detection (TBAD), and Statistical Pattern Matching (SPM) have been widely used; however, these methods suffer from high false-alarm rates, limited adaptability to dynamic physiological variations, and reduced accuracy in noisy sensor environments. To overcome these limitations, this research introduces the Machine Learning-Driven Adaptive Health Detection Algorithm (ML-AHDA). The proposed ML-AHDA improves overall classification accuracy by 31%, reduces false-alarm rate by 27%, and enhances early disease prediction stability by 22% when compared to RBHM, TBAD, and SPM. By learning complex nonlinear patterns, adapting to user-specific physiology, and applying dynamic model updates, MLAHDA provides a significantly more reliable and intelligent health monitoring solution for next-generation wearable IoT healthcare applications.