The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in healthcare has revolutionized patient monitoring and disease management. This research explores the development and optimization of AI-based remote health monitoring systems, leveraging IoT devices and machine learning algorithms. The study focuses on improving diagnostic accuracy, real-time data processing, data security, and system integration for enhanced patient care. By addressing challenges such as data reliability, privacy concerns, and computational constraints, this research proposes a novel approach to continuous health monitoring. Several machine learning models, including Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), are employed to analyze health metrics such as heart rate, blood pressure, and ECG data. The findings indicate that the Random Forest model outperforms other algorithms in terms of accuracy, precision, and recall, demonstrating its potential for real-time health monitoring applications. This work provides valuable insights into the application of AI-driven health systems, offering a foundation for future advancements in personalized, cost-effective healthcare solutions.
Ramzan et al. (Thu,) studied this question.
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