Development and Implementation of an Intelligent Health Monitoring System using IoT and Advanced Machine Learning Techniques

Abstract

Healthcare practices have a tremendous amount of potential to change as a result of the convergence of IoT technologies with cutting-edge machine learning. This study offers an IoT-connected sensor-based Intelligent Health Monitoring System for real-time patient health assessment. Our system offers continuous health monitoring and early anomaly identification by integrating temperature, blood pressure, and ECG sensors. The Support Vector Machine (SVM) model proves to be a reliable predictor after thorough analysis, obtaining astounding accuracy rates of 94% for specificity, 95% for the F1 score, 92% for recall, and 94% for total accuracy. These outcomes demonstrate how well our system performs when it comes to providing precise and timely health predictions. Healthcare facilities can easily integrate our Intelligent Health Monitoring System as part of the practical application of our research. Real-time sensor data can be used by doctors to proactively spot health issues and provide prompt interventions, improving the quality of patient care. This study's integration of advanced machine learning and IoT underlines the strategy's disruptive potential for transforming healthcare procedures. This study provides the foundation for a more effective, responsive, and patient-centered healthcare ecosystem by employing the potential of connected devices and predictive analytics.