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@article{187265,
        author = {RISHDHARAN M and SIVABALAN G and YUVARAJ S and VIKRAM V},
        title = {DIER RECOMMENDATIONS SYSTEM FOR ATHELETES USING MACHINE LEARNING IN MATLAB},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6264-6272},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187265},
        abstract = {Athlete performance is strongly dependent on optimal nutrition, but most athletes struggle to maintain proper dietary balance due to differences in body metabolism, training intensity, sport category, and fitness goals. Manual diet planning requires expert nutritionists, is time-consuming, and cannot scale to large user groups. This paper presents a **Machine Learning–based Diet Recommendation System for Athletes using MATLAB**, designed to automate personalized nutrition planning. The system integrates athlete profiling, macronutrient estimation, food-nutrient mapping, supervised learning models, and optimization techniques to generate customized diet plans. Using MATLAB’s Classification Learner Toolbox, Regression Learner, and Optimization Toolbox, the system predicts caloric needs, recommends macronutrient ratios, and maps them to optimal food combinations. Experimental results show highly accurate predictions of calorie expenditure (92%), protein estimation accuracy (89%), and strong recommendation consistency across various sport categories. The proposed system demonstrates that machine-learning-driven nutrition planning can significantly enhance dietary personalization, reduce manual effort, and support athletes in achieving peak performance.},
        keywords = {—Machine Learning, Sports Nutrition, Diet Recommendation, MATLAB, Athlete Performance, Regression Models, Optimization.},
        month = {November},
        }