Adaptive Ai Recommendation Engine For Personalized Diet And Health Behavior Change

Dr. V. Veerakumaran; Anju M. A; K. Kavitha; Dr. M. A. Raja; Dr. Sheeba E; S. Pragadeswaran

doi:10.29284/y463wd33

Authors

Dr. V. Veerakumaran Department of Computer Science and Data Science / Microbiology ,Nehru Arts and Science College (Autonomous), Coimbatore, Tamil Nadu, India.
Anju M. A Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology, Coimbatore – 641105, Tamil Nadu, India.
K. Kavitha Department of Electronics and Communication Engineering Rathinam Technical Campus, Coimbatore, Tamil Nadu, India
Dr. M. A. Raja Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology, Coimbatore – 641105, Tamil Nadu, India.
Dr. Sheeba E Department of Computer Science and Data Science / Microbiology ,Nehru Arts and Science College (Autonomous), Coimbatore, Tamil Nadu, India.
S. Pragadeswaran Department of Electronics and Communication Engineering Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India.

DOI:

https://doi.org/10.29284/y463wd33

Keywords:

Adaptive Artificial Intelligence; Personalized Nutrition; Diet Recommendation System; Health Behavior Change; Reinforcement Learning; Machine Learning; Explainable AI; Preventive Healthcare.

Abstract

Personalized dietary guidance and sustained health behavior change remain challenging due to individual variability in metabolism, lifestyle, preferences, and adherence patterns. This paper proposes an Adaptive Artificial Intelligence (AI) Recommendation Engine designed to deliver dynamic, personalized diet and health behavior interventions by integrating multi-modal user data, including demographic profiles, dietary intake, physical activity, physiological indicators, and behavioral feedback. The proposed system employs a hybrid learning framework combining machine learning–based nutritional profiling, reinforcement learning–driven recommendation adaptation, and explainable AI mechanisms to continuously refine dietary plans and behavioral nudges in response to user progress and compliance levels. A feedback-aware optimization module dynamically adjusts caloric intake, macro-nutrient distribution, and lifestyle recommendations while minimizing user fatigue and recommendation drift. Experimental evaluation using real-world and benchmark health datasets demonstrates improved adherence rates, dietary accuracy, and long-term behavior retention compared to conventional rule-based and static recommendation systems. The results indicate that the proposed adaptive AI engine offers a scalable, user-centric solution for personalized nutrition management and preventive healthcare applications.

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