Ai-Based Smart Health Monitoring Using Machine Learning & Iot

Abstract

Growing requirements for continuous health monitoring have spurred the adoption of Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing in modern healthcare. This paper demonstrates an end-to-end smart health monitoring system that leverages wearable IoT sensors for real-time physiological monitoring, cloud infrastructure for scalable processing, and high-performance AI models for anomaly detection and predictive diagnosis. The architecture in question leverages edge computing for low-latency preprocessing and uses strong machine learning algorithms such as KNN, Autoencoders, Isolation Forest, and LSTM for the detection of abnormal health patterns and the prediction of medical conditions. Multi-layered encryption, OAuth2.0 authorization, and GDPR-compliant processing guarantee security. Experimental studies with real-world and synthetic data validate high accuracy, quick response, and scalability. The system enhances active care delivery, improves patient health, and simplifies healthcare costs through telemedicine diagnosis and patient-specific recommendations. Future advancements with federated learning, explainable AI, and blockchain deployment will further enhance the transparency, trustworthiness, and flexibility of the system.