Personalized Diet Plan Recommendation based on Health Data

Abstract This study focuses on the development of a personalized diet plan recommendation framework using healthcare data that captures the prevalence of major chronic and lifestyle-related diseases such as asthma, cancer, diabetes, hypertension, high blood sugar, obesity, anemia, tuberculosis, cardiovascular disease, and thyroid disorders. By analyzing population-level disease percentages across states and regions, the research aims to identify dominant health conditions and recommend disease-specific dietary strategies. For example, iron-rich diets can be suggested for anemia, fiber and protein-based diets for diabetes, and low-sodium diets for hypertension. The proposed framework bridges the gap between disease prevalence and nutritional intervention, offering a data-driven approach for preventive healthcare and improved disease management at both regional and individual levels. Keywords: Personalized diet plan, Healthcare data, Machine learning, Disease prediction, Nutrition recommendation.