Authors - Gauree Prabhakar Sayam, Supriya Narad Abstract - Chronic non-communicable diseases like diabetes, heart disease, and obesity continue to increase globally, comprising 74% of all deaths, even as noted by the World Health Organization in the 2025 progress monitor on non-communicable diseases. This work describes the design and deployment of Health Risk Advisor, an AI (artificial intelligence) web application powered by machine learning that predicts early risks and provides personalized recommendations on disease prevention. The integration of ensemble models such as Random Forest and XG-Boost into a rule-based advisory engine allows the application to achieve more than 90% accuracy in making risk classifications, addressing access barriers to healthcare in underserved regions, such as rural India. From architecture and design, healthcare applications and benefits, to ethical AI challenges and considerations, this work discusses every aspect of the new technology using diverse sets of datasets that inform practices as well as recommend ethical AI. Evaluations showed reductions of the burden from NCDs between 20-30% by engaging the application in a preventive healthcare intervention, which is aligned to global health equity goals.
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