Smart Healthcare Systems: A Synergistic Approach Using Machine Learning and IOT Integration

The integration of the Internet of Things (IoT) with machine learning is revolutionizing healthcare by introducing real-time and predictive health monitoring solutions. This research presents a comprehensive health monitoring system that acquires physiological data using IoT sensors accurately using robust machine learning algorithms. The system processes physiological parameters, including pulse rate and body temperature, through effective normalization and outlier removal techniques in real time, ensuring data reliability. Five machine learning classifiers, including Support Vector Machine (SVM), Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Decision Tree, were implemented and evaluated on a dataset of 4287 patient samples. Naïve Bayes achieved the highest accuracy of 97%, with superior performance metrics across precision (71%), recall (75%), F1-score (73%), and False Positive Rate (0.91%). The system demonstrates low latency (≈25ms) for data transmission and effective scalability, making it suitable for diverse healthcare settings, particularly in resource-limited areas. Key findings validate the effectiveness of the system in enhancing early diagnosis and personalized care. This research contributes to healthcare technology advancement by providing a scalable, efficient, and reliable framework for predictive health monitoring.