ABSTRACT
This study introduces a novel health monitoring and management system based on the Internet of Things, with the goal of eliminating healthcare disparities in rural areas. We used advanced machine learning techniques such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) to predict patients‘ health status with remarkable accuracy from a massive data set of 2,000 patient records. In multiple efficacy trials, the ANN model performed best, with a 91.2% accuracy rate. It also achieved higher recall, precision, and F1 scores. The study emphasises the importance of early disease detection and proactive health monitoring, which would significantly improve the accessibility of medical facilities and health care. It also demonstrates that machine learning could be useful in the healthcare industry. Our system eliminates rural neglect by combining real-time sensor data with predictive analytics, allowing medical personnel to intervene quickly and save many patients’ lives while also controlling their condition from a distance. Furthermore, the system's broader applications in healthcare include patient outcomes and healthcare delivery in a variety of care settings, including rural ones.
