International Journal of Multidisciplinary Engineering Sciences
Peer Reviewed Open Access Journal
Peer Reviewed Open Access Journal
The rapid growth of Internet of Things (IoT) technologies and Artificial Intelligence (AI) has significantly transformed modern healthcare systems by enabling intelligent monitoring, real-time data analysis, and predictive disease diagnosis. Conventional healthcare systems often experience delays in patient monitoring and disease identification due to limited accessibility and manual analysis procedures. This research presents an AI-based smart healthcare monitoring and predictive disease detection framework integrating IoT sensors with machine learning techniques for continuous health assessment. The proposed system collects physiological parameters such as heart rate, body temperature, blood pressure, oxygen saturation, and glucose levels through IoT-enabled wearable devices. The gathered data are processed using machine learning algorithms to identify abnormalities and predict potential diseases at an early stage. The framework employs cloud-based storage and analytical models to improve healthcare accessibility, accuracy, and remote patient management. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and efficient monitoring compared with conventional healthcare systems. The proposed model provides a scalable, cost-effective, and intelligent healthcare solution suitable for smart hospitals and remote healthcare environments.
Artificial Intelligence, Internet of Things, Smart Healthcare, Machine Learning, Predictive Analytics, Remote Patient Monitoring, Disease Detection
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