AI-enhanced Healthcare IoT System: Advanced ML Detection and Classification Algorithms for Real-Time Cardiovascular Monitoring

Cardiovascular diseases (CVDs), including coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions affecting the heart and blood vessels, are identified by both the Pan American Health Organization and the World Health Organization as the leading cause of global mortality, underscoring their significant impact on health worldwide. In this paper we utilize emerging technologies in the fields of Internet of Medical Things (IoMT) and AI/Machine Learning (ML) and propose an end-to-end prototype platform for real-time detection, classification, prediction, and monitoring of cardiovascular anomalies. In addition we introduce Cardio-ECG-Heart Arrhythmia Algorithms using advanced AI/ML, combining mathematical-statistical and computational techniques to intelligently detect critical conditions related to CVDs and arrhythmias. The proposed innovative AI/ML Peak Detection algorithm based on adaptive thresholds, coupled with the ML Decision Tree Classification algorithm, has been tested with numerous ECG signals and exhibits remarkable performance, achieving exceptional precision, accuracy, sensitivity, specificity, and F1 Score rates between 99% and 100%.