<![CDATA[Early detection of chronic diseases remains a major public health challenge, particularly in urban communities where lifestyle-related risk factors are increasingly prevalent. This study proposes and evaluates an artificial intelligence (AI)-enabled framework for the early detection of chronic diseases using wearable sensor data collected in Quezon City, Philippines. The research integrates multimodal physiological and behavioral signals, including heart rate, physical activity, sleep patterns, and other relevant biosensor indicators, to identify early risk patterns associated with chronic conditions such as hypertension, diabetes, and cardiovascular disease. A supervised machine learning approach was employed to preprocess, analyze, and classify wearable sensor data obtained from [number] participants. Feature engineering and model optimization techniques were applied to improve predictive performance and reduce noise from real-world sensor streams. The developed model was evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The findings indicate that the proposed AI-based system achieved promising predictive performance in distinguishing individuals at elevated risk of chronic disease, demonstrating the feasibility of wearable technologies as scalable tools for preventive healthcare. In addition, the study highlights the potential of continuous, non-invasive monitoring to support earlier clinical intervention and personalized health management in resource-constrained urban settings. This research contributes to the growing field of digital health by offering an AI-driven, data-informed approach to chronic disease surveillance and early risk detection in the Philippine context.]]>
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