DESIGN OF AN INTELLIGENT IOT-DRIVEN REAL-TIME CONTINUOUS REMOTE PATIENT CARE FRAMEWORK USING MACHINE LEARNING

Abstract

The combination of Artificial Intelligence (AI) and the Internet of Things (IoT) has significantly transformed modern healthcare, particularly in the areas of patient monitoring and disease management. This research focuses on designing and improving an AI-enabled remote health monitoring system that utilizes IoT devices along with machine learning techniques. The primary aim is to enhance diagnostic accuracy, enable real-time data analysis, ensure data security, and achieve seamless system integration to improve overall patient care. The study also addresses key challenges such as maintaining data reliability, protecting patient privacy, and managing computational limitations. To support continuous health monitoring, various machine learning algorithms including Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) are applied to analyze important health indicators such as heart rate, blood pressure, and ECG signals. The experimental results show that the Random Forest algorithm performs better than the other models in terms of accuracy, precision, and recall. This highlights its effectiveness for real-time healthcare applications. Overall, this research demonstrates the potential of AI-driven health monitoring systems and provides a strong foundation for developing more personalized, efficient and affordable healthcare solutions in the future.