An Efficient and Secure Framework for Smart Healthcare Using IoT and Machine Learning

The healthcare systems worldwide are challenged with the increased demand for rapid and efficient patient care. In this context, an advanced solution is required to meet the growing needs. This paper presents an intelligent healthcare framework integrating IoT devices with machine learning algorithms to enable real-time predictive patient monitoring. It comprises a four-layer architecture: data acquisition from IoT sensors, data preprocessing, analytics for predictive modeling, and an application layer for user interactions. Validated with data from the MIMIC-III and PhysioNet datasets along with 12 months of IoT sensor data from a university medical center, the framework achieved 96% prediction accuracy with precision, recall, and ROC AUC scores of 0.95, 0.96, and 0.98, respectively. The system had a great response time of 200 milliseconds. It has greatly improved early detection by 36.9% and reduced false alarms by 68%. False alarms decreased from 25% to 8%, and response time was reduced from 45 to 12 minutes, or 73.3 %. Resource utilization analysis reflects optimal performance since the analytics layer hit a spike at 45% CPU utilization, thereby pointing out optimal load management. Patient satisfaction also rose to 22.7%, increasing from 75% to 92%, reflecting the framework's impact on care quality. All these results render the framework an effective and scalable solution that optimizes healthcare resources while improving care for the patient.