AN AI-DRIVEN FRAMEWORK FOR CONTINUOUS HEALTH MONITORING AND EARLY DISEASE DETECTION

Abstract

Smart healthcare systems have gained significant importance due to the increasing demand for continuous patient monitoring and early disease detection. This paper presents an intelligent smart healthcare monitoring system using machine learning techniques to analyze physiological data collected from patients. The proposed system integrates data acquisition, preprocessing, feature extraction, and predictive analytics to provide real-time health insights. Machine learning models are employed to classify health conditions and detect anomalies accurately. The system enhances decision-making by assisting healthcare professionals with timely alerts. Experimental evaluations demonstrate improved prediction accuracy and reduced response time. The results validate the effectiveness of the proposed framework in real-world healthcare scenarios. The system is scalable, cost-effective, and suitable for remote patient monitoring. It also supports preventive healthcare through early risk identification. Overall, the proposed approach contributes to intelligent, data-driven healthcare management.