Optimizing Metabolic Health Tracking with SVM Classification and Real-Time IoT Data

Combining innovative machine learning methods with real-time data from the Internet of Things (IoT) has great potential for better metabolic health management. Applying Support Vector Machine (SVM) classification to Synthetic Metabolic Dataset this research investigates ways to optimize metabolic health monitoring. The techniques aim to improve the speed and accuracy of health evaluations by using wearable IoT sensors that continually monitor physiological factors, including glucose levels, heart rate, and activity metrics. The SVM classifier is used to process and evaluate data. It can accurately discriminate between different metabolic states and predict potential health hazards. Metabolic health care that is both proactive and personalized to the individual may benefit from this real-time processing capacity, which enables quick feedback and action. The SVM attained an accuracy of 96.5%, precision of 80.95%, and recall of 85%, enhancing metabolic health prediction with real-time IoT data from 500 individuals.