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.

I. Introduction

With global demographics shifting towards an older population, the need for new healthcare solutions has never been more urgent. Imagine an older person, living independently but vulnerable to health issues that can strike at any time. Wearable technology is changing how we monitor and manage the health of these individuals, providing a safety net so they can live well without constant in-person supervision. These devices with advanced machine learning algorithms give us unprecedented opportunity to improve elderly care by delivering real time data, personalized interventions and predictive analytics.