Automated Personalized Health Analytics using IoT and Machine Learning Algorithms

Abstract

This research work develops and implements an integrated system using IoT technologies and machine learning algorithm to measure Body Mass Index (BMI) and perform health analytics in hospitals. Obesity, overweight, and chronic diseases are major public health issues. Healthcare workers need accurate and quick BMI readings to monitor weight status and health issues. To overcome these limitations, the proposed system uses IoT sensors to automate BMI evaluations and collect real-time health data. Wireless IoT devices interface with hospital information systems. The Support Vector Machine (SVM) algorithm analyzes BMI, physical activity, and vital sign data. It can identify and predict BMI values from data. SVM can predict BMI for customized weight loss recommendations. The proposed setup helps hospitals to automate BMI measuring, thus reducing medical staff burden and manual intervention. Real-time monitoring and analysis enable early identification and individualized treatment of obesity-related health concerns. IoT devices with the SVM algorithm enable data-driven decision-making, improving patient outcomes and healthcare efficiency.