ML Based Multi-Faceted Health Monitoring Devices: Enhancing Elderly Well-Being

This research paper explores the application, implementation, and assessment of health parameters in the elderly healthcare sector using Random Forest and Support Vector Machine algorithms in Machine Learning. These algorithms increase the accuracy and predictive capabilities of health monitoring devices that analyze heart rate, fall risks, stress levels, and hypertension in the elderly in real time. The integration of IoT technology into the system ensures continuous and efficient monitoring with reliable communication and connectivity. The sensors are located to pick up essential health data, which provides the baseline for a deep analysis of health trends. This paper elaborates on how machine learning predicts and monitors health parameters in the elderly population.