A Study on the Personalized Wellness Diet Recommendation Syestem

A Study on the Personalized Wellness Diet Recommendation Syestem

We developed an AI-based personalized diet recommendation system to address the increasing demand for customized health management solutions. Modern lifestyles often lead to poor eating habits and wellness issues such as fatigue, stress, and poor sleep, which require more adaptive and personalized approaches to nutrition. We designed the system to provide daily meal suggestions based on wellness indicators including sleep patterns, physical activity, stress levels, and dietary preferences. A content-based filtering algorithm was implemented to match user profiles with food nutrient data. To evaluate the system's performance, we conducted a simulation with 10 synthetic user profiles. Each profile was assigned a wellness goal-sleep improvement, stress reduction, or energy enhancement-and received a tailored meal recommendation. The system then assessed the nutritional completeness of the meal and selectively recommended dietary supplements only when essential nutrients were missing. The results showed that the system successfully aligned recommended foods with each user's wellness goal, and recommended supplements only when essential nutrients were missing from the meal. Our approach demonstrates the practicality and adaptability of AI in preventive healthcare and personalized nutrition planning.