Citation :

Usha Kamale, M. Pujashree, T. Siri Chandana, A. Karthik, "Target Diet Recommendation for Health Optimization using Random Forest and Rule-Based AI," International Journal of Computer & Organization Trends (IJCOT), vol. 16, no. 2, pp. 21-26, 2026. Crossref, https://doi.org/10.14445/22492593/IJCOT-V16I2P303

Abstract

Maintaining a balanced and nutritious diet is essential for preventing lifestyle-related diseases and enhancing overall well-being. However, conventions diet planning approaches often provide generalized guidelines that do not account for individual health conditions, allergies, lifestyle habits, or personal food preferences. Because of this, many individuals follow diet plans that may not be suitable for their specific nutritional needs. Recent advancements in artificial intelligence and machine learning have enabled systems to generate personalized recommendations. This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information. The system collects parameters such as age, gender, height, weight, dietary preferences, allergies, and medical conditions. A Random Forest algorithm is employed to determine the most appropriate diet category. A rule-based filtering mechanism is applied to ensure that the recommended diet follows medical guidelines and avoids foods that may be harmful. The system also calculates important health indicators such as Body Mass Index (BMI), Basal Metabolic Rate (BMR), and daily calorie requirements to support accurate nutritional analysis. In addition, an AI chat-bot is integrated to provide dietary guidance and answer user queries related to nutrition. The system is implemented as a web application using the Flask framework and demonstrates the ability to generate safe and personalized diet recommendations.

Keywords

Artificial Intelligence, Diet Recommendation System, Personalized Nutrition, Machine Learning, Random Forest, Healthcare Informatics.

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