Machine Learning for Personalized Healthcare Monitoring Using IoT Devices

Abstract

IoT-powered healthcare monitoring devices generate vast amounts of patient data, which can be analyzed using machine learning to offer personalized health insights and early disease detection. This paper explores ML techniques such as deep learning, ensemble models, and federated learning for analyzing wearable sensor data. It discusses challenges like data privacy, algorithm bias, and real-time processing limitations. Case studies highlight ML-driven healthcare monitoring applications, demonstrating improved patient outcomes, early disease prevention, and efficient remote health monitoring.