Year: 2025
Type: article
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.
DOI: 10.55041/ijsrem52628
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4414203409
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