Smart Healthcare Systems using IoT and Random Forest Algorithm for Diabetes Prediction and Management

The amalgamation of IoT with machine learning techniques presents an innovation in the healthcare sector by offering novel approaches to disease prediction and management. The work describes the design of a smart healthcare system using IoT devices and the Random Forest algorithm to enhance diabetes prediction and management. The proposed system will make health parameter monitoring real- time with the help of the sensors, thus enabling early diagnosis of diabetes for effective personalized interventions against the disease. The Random Forest algorithm, which is known for its robustness and high accuracy, processes data from IoT sensors for making sound predictions. Not only does this enhance diagnostic accuracy, but it also supports continuous monitoring and proactive decision-making which can be brought into applications to address the emerging need for efficient healthcare solutions. The research study describes the potential of IoT-based, supervised learning-powered systems to change chronic disorder management, giving the path for intelligent and more adapting healthcare environment. However, the system’s effectiveness may vary based on factors such as sensor placement accuracy rate, network reliability, and data privacy concerns, which shows the requirement for further validation and risk mitigation in real-world deployments