<![CDATA[This study proposes a wearable device-based health monitoring system integrated with artificial intelligence (AI) predictive analytics to enable continuous, real-time, and proactive healthcare management. The system utilizes wearable sensors to collect physiological and activity data, including heart rate, blood oxygen saturation (SpO?), body temperature, and movement patterns. These data are transmitted through IoT-based communication to a cloud platform, where they undergo preprocessing, feature extraction, and analysis using machine learning and deep learning models. The proposed approach incorporates algorithms such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to perform disease prediction, anomaly detection, and risk scoring. Experimental results demonstrate that the models achieve high performance across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, with LSTM showing superior performance in handling time-series data. The system effectively supports real-time monitoring, enabling early detection of potential health risks and providing timely alerts to users and healthcare providers. Compared to existing systems, the proposed framework offers enhanced predictive capabilities, improved responsiveness, and better integration of wearable technology with AI-driven analytics. The findings highlight the significant potential of combining wearable devices and AI in advancing healthcare innovation, particularly in remote patient monitoring, telemedicine, and preventive medicine. Despite challenges related to data privacy, device limitations, and computational requirements, this research demonstrates a scalable and intelligent solution for modern healthcare systems, emphasizing the critical role of predictive analytics in the future of preventive healthcare.]]>
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