International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rise of obesity and metabolic disorders highlights the limitations of generalized dietary advice, which often fails to meet individual health needs. At the same time, health tracking applications and self-reported data provide valuable insights into individuals’ heart rate, physical activity, sleep patterns, and dietary intake. This research presents a machine learning–based personalized nutrition recommendation system that integrates biometric, lifestyle, and dietary data to provide customized diet plans for individuals. By analyzing patterns in user behavior and correlating them with health risk factors, the system predicts potential nutritional deficiencies or risks and generates actionable recommendations tailored to each user. The approach leverages data-driven modeling to bridge the gap between raw health data and effective, personalized dietary guidance. Experimental evaluation on synthetic and public datasets demonstrates that the system can accurately identify individual health risks and suggest targeted nutritional adjustments, promoting preventive healthcare and overall well-being.

Keywords: Personalized Nutrition, Machine Learning in Healthcare, Dietary Recommendation Systems, Preventive Healthcare, Biometric Data Analytics, Health Risk Prediction, Individualized Diet Planning.

Downloads: | DOI: 10.17148/IJARCCE.2025.141190