Authors:
J. Angelin Jeba, S. Rubin Bose, R. Regin, O. Jeba Singh, S. Silvia Priscila, Mykhailo Paslavskyi
Addresses:
Department of Electronics and Communication Engineering, S. A. Engineering College, Chennai, Tamil Nadu, India. School of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Center for Academic Research, Alliance University, Bengaluru, Karnataka, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Computer Science, Ukrainian National Forestry University, Lviv, Lviv Oblast, Ukraine.
Abstract:
A balanced diet prevents lifestyle-related diseases like obesity, diabetes, hypertension, and cardiovascular disease and improves physical and mental health. Traditional diet-planning methods, which use general nutritional guidelines or dietician consultations, often fail to account for an individual's unique eating habits, lifestyle patterns, and health conditions, making them less effective at promoting sustainable, healthy behaviour. This study provides a machine learning–based Diet Recommendation System that tailors dietary recommendations to user data to overcome these constraints. The system evaluates dietary habits (e.g., frequency of fruits, vegetables, and fast food), physical activity levels, exercise routines, and other lifestyle indicators, as well as demographics such as age, gender, height, weight, and BMI. These inputs ensure that each piece of advice aligns with the user's nutritional and health goals. The suggested system classifies Balanced Diet, Weight-Loss Diet, High-Protein Diet, and Low-Carbohydrate Diet using dependable machine learning methods, such as Random Forest, XGBoost, and SVM. To ensure consistency and accuracy, models are trained and evaluated using a structured, preprocessed dataset. The system improves diet planning by integrating data-driven intelligence with nutrition research, enhancing accessibility, scalability, and effectiveness.
Keywords: Diet Prediction; Machine Learning; Dietary Habits; Lifestyle Patterns; Random Forest; Support Vector Machine (SVM); Health Recommendation; Body Mass Index (BMI).
Received: 19/11/2024, Revised: 08/03/2025, Accepted: 03/06/2025, Published: 09/12/2025
DOI: 10.64091/ATIHL.2025.000206
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 4 , Pages: 187-197