A Bio-Inspired AI Approach to Personalized Dietary Planning for Chronic Disease Prevention - Silva, Thalyson G. N. da; Campos, Gustavo A. L. de; Amaro Júnior, Bonfim; Barros, Ana Luiza B. de Paula (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.16268, 2025)
This study presents a bio-inspired approach to individualized dietary planning for the prevention of chronic non-communicable diseases. Using real-world data from the Brazilian Household Budget (POF) and National Dietary Surveys (INA), the algorithm adjusts each individual’s recorded diet to meet nutritional adequacy targets based on WHO guidelines. Cultural and socioeconomic dietary patterns are preserved by restricting substitutions to regionally available foods. The heuristic search applies mutation, crossover, and fitness-based selection to iteratively refine food combinations and improve intake of 25 key nutrients. Results from over 400 individuals across demographic groups showed significant gains in nutrient adequacy, increasing from approximately 72% to 99.8% on average. The approach also revealed persistent gaps in the Brazilian diet, especially in fruit, vegetable, and dairy consumption. This work highlights the potential of combining operational research and public health nutrition to provide scalable, culturally sensitive, and data-driven strategies for dietary improvement and chronic disease prevention.