A Bio-Inspired AI Approach to Personalized Dietary Planning for Chronic Disease Prevention
Resumo
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.
Palavras-chave:
Artificial Intelligence, Heuristic Optimization, Artificial Intelligence in Health, Dietary Planning, Chronic Diseases
Referências
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E. Verly, D. M. Marchioni, M. C. Araujo, E. D. Carli, D. C. R. S. d. Oliveira, E. M. Yokoo, R. Sichieri, and R. A. Pereira, “Evolution of energy and nutrient intake in brazil between 2008–2009 and 2017–2018,” Revista de Saúde Pública, vol. 55, no. Supl 1, p. 5s, 2021.
G. J. Petot, C. Marling, and L. Sterling, “An artificial intelligence system for computer-assisted menu planning,” Journal of the American Dietetic Association, vol. 98, no. 9, pp. 1009–1014, 1998.
W. Raghupathi and V. Raghupathi, “An empirical study of chronic diseases in the united states: a visual analytics approach to public health,” International journal of environmental research and public health, vol. 15, no. 3, p. 431, 2018.
S. M. Fanelli, S. S. Jonnalagadda, J. L. Pisegna, O. J. Kelly, J. L. Krok-Schoen, and C. A. Taylor, “Poorer diet quality observed among us adults with a greater number of clinical chronic disease risk factors,” Journal of primary care & community health, vol. 11, p. 2150132720945898, 2020.
T. F. Soares, M. C. Escarpinati, and P. H. Gabriel, “Multi-objective optimization applied to diet planning for people with diabetes,” Pesquisa Operacional, vol. 44, p. e285156, 2024.
K. Zioutos, H. Kondylakis, and K. Stefanidis, “Healthy personalized recipe recommendations for weekly meal planning,” Computers, vol. 13, no. 1, p. 1, 2023.
K. S. Coelho, E. B. Giuntini, O. D. Betazzi, M. A. Horst, J. da Silva Dias, B. D. G. de Melo Franco, E. W. de Menezes, F. M. Lajolo, and E. Purgatto, “Nutripersona: Conception of a computational tool for elaboration of personalized menu from a brazilian food composition database,” Journal of Food Composition and Analysis, vol. 123, p. 105582, 2023.
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S. G. Garille and S. I. Gass, “Stigler’s diet problem revisited,” Operations Research, vol. 49, no. 1, pp. 1–13, 2001.
M. R. S. Reis, “Programação linear e o problema da dieta,” Master’s thesis, Universidade Federal de Sergipe, 2023.
E. Verly-Jr, R. Sichieri, N. Darmon, M. Maillot, and F. M. Sarti, “Planning dietary improvements without additional costs for low-income individuals in brazil: linear programming optimization as a tool for public policy in nutrition and health,” Nutrition journal, vol. 18, pp. 1–12, 2019.
E. Verly-Jr, A. da Silva Pereira, E. S. Marques, P. M. Horta, D. S. Canella, and D. B. Cunha, “Reducing ultra-processed foods and increasing diet quality in affordable and culturally acceptable diets: a study case from brazil using linear programming,” British Journal of Nutrition, vol. 126, no. 4, pp. 572–581, 2021.
E. Verly-Jr, A. M. de Carvalho, D. M. L. Marchioni, and N. Darmon, “The cost of eating more sustainable diets: A nutritional and environmental diet optimisation study,” Global public health, vol. 17, no. 6, pp. 1073–1086, 2022.
B. L. R. Milagres, “Uma abordagem multi-objetivo do problema de planejamento de cardápios voltada para escolas de ensino básico de minas gerais.” Master’s thesis, Universidade Federal de Ouro Preto, 2023.
E. Kaldirim and Z. Kose, “Application of a multi-objective genetic algorithm to the modified diet problem,” in Genetic and Evolutionary Computation Conference (GECCO), vol. 6, 2006.
C. Türkmenoǧlu, A. S¸ . Etaner Uyar, and B. Kiraz, “Recommending healthy meal plans by optimising nature-inspired many-objective diet problem,” Health Informatics Journal, vol. 27, no. 1, p. 1460458220976719, 2021.
E. M. Porras, A. C. Fajardo, and R. P. Medina, “Solving dietary planning problem using particle swarm optimization with genetic operators,” in Proceedings of the 3rd international conference on machine learning and soft computing, 2019, pp. 55–59.
P. Ducrot, C. Méjean, V. Aroumougame, G. Ibanez, B. Allès, E. Kesse-Guyot, S. Hercberg, and S. Péneau, “Meal planning is associated with food variety, diet quality and body weight status in a large sample of french adults,” International journal of behavioral nutrition and physical activity, vol. 14, pp. 1–12, 2017.
W. D. H. Wijekoon and S. Harshanath, “Meal preparation algorithm for diabetic patients using machine learning,” Sri Lankan Journal of Applied Sciences, vol. 1, no. 02, pp. 27–33, 2023.
J. Who and F. E. Consultation, “Diet, nutrition and the prevention of chronic diseases,” World Health Organ Tech Rep Ser, vol. 916, no. i-viii, pp. 1–149, 2003.
S. C. on the Scientific Evaluation of Dietary Reference Intakes, S. on Interpretation, U. of Dietary Reference Intakes, S. on Upper Reference Levels of Nutrients, P. on the Definition of Dietary Fiber, and P. on Macronutrients, Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. National Academies Press, 2005.
T. Beal, E. Massiot, J. E. Arsenault, M. R. Smith, and R. J. Hijmans, “Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes,” PloS one, vol. 12, no. 4, p. e0175554, 2017.
E. Verly, D. M. Marchioni, M. C. Araujo, E. D. Carli, D. C. R. S. d. Oliveira, E. M. Yokoo, R. Sichieri, and R. A. Pereira, “Evolution of energy and nutrient intake in brazil between 2008–2009 and 2017–2018,” Revista de Saúde Pública, vol. 55, no. Supl 1, p. 5s, 2021.
G. J. Petot, C. Marling, and L. Sterling, “An artificial intelligence system for computer-assisted menu planning,” Journal of the American Dietetic Association, vol. 98, no. 9, pp. 1009–1014, 1998.
W. Raghupathi and V. Raghupathi, “An empirical study of chronic diseases in the united states: a visual analytics approach to public health,” International journal of environmental research and public health, vol. 15, no. 3, p. 431, 2018.
S. M. Fanelli, S. S. Jonnalagadda, J. L. Pisegna, O. J. Kelly, J. L. Krok-Schoen, and C. A. Taylor, “Poorer diet quality observed among us adults with a greater number of clinical chronic disease risk factors,” Journal of primary care & community health, vol. 11, p. 2150132720945898, 2020.
T. F. Soares, M. C. Escarpinati, and P. H. Gabriel, “Multi-objective optimization applied to diet planning for people with diabetes,” Pesquisa Operacional, vol. 44, p. e285156, 2024.
K. Zioutos, H. Kondylakis, and K. Stefanidis, “Healthy personalized recipe recommendations for weekly meal planning,” Computers, vol. 13, no. 1, p. 1, 2023.
K. S. Coelho, E. B. Giuntini, O. D. Betazzi, M. A. Horst, J. da Silva Dias, B. D. G. de Melo Franco, E. W. de Menezes, F. M. Lajolo, and E. Purgatto, “Nutripersona: Conception of a computational tool for elaboration of personalized menu from a brazilian food composition database,” Journal of Food Composition and Analysis, vol. 123, p. 105582, 2023.
T. G. N. da Silva, G. A. L. de Campos, B. A. Júnior, and A. L. B. d. P. Barros, “Bio-inspired dietary optimization for chronic disease prevention using brazilian national surveys,” anais do LVI Simpósio Brasileiro de Pesquisa Operacional.
S. G. Garille and S. I. Gass, “Stigler’s diet problem revisited,” Operations Research, vol. 49, no. 1, pp. 1–13, 2001.
M. R. S. Reis, “Programação linear e o problema da dieta,” Master’s thesis, Universidade Federal de Sergipe, 2023.
E. Verly-Jr, R. Sichieri, N. Darmon, M. Maillot, and F. M. Sarti, “Planning dietary improvements without additional costs for low-income individuals in brazil: linear programming optimization as a tool for public policy in nutrition and health,” Nutrition journal, vol. 18, pp. 1–12, 2019.
E. Verly-Jr, A. da Silva Pereira, E. S. Marques, P. M. Horta, D. S. Canella, and D. B. Cunha, “Reducing ultra-processed foods and increasing diet quality in affordable and culturally acceptable diets: a study case from brazil using linear programming,” British Journal of Nutrition, vol. 126, no. 4, pp. 572–581, 2021.
E. Verly-Jr, A. M. de Carvalho, D. M. L. Marchioni, and N. Darmon, “The cost of eating more sustainable diets: A nutritional and environmental diet optimisation study,” Global public health, vol. 17, no. 6, pp. 1073–1086, 2022.
B. L. R. Milagres, “Uma abordagem multi-objetivo do problema de planejamento de cardápios voltada para escolas de ensino básico de minas gerais.” Master’s thesis, Universidade Federal de Ouro Preto, 2023.
E. Kaldirim and Z. Kose, “Application of a multi-objective genetic algorithm to the modified diet problem,” in Genetic and Evolutionary Computation Conference (GECCO), vol. 6, 2006.
C. Türkmenoǧlu, A. S¸ . Etaner Uyar, and B. Kiraz, “Recommending healthy meal plans by optimising nature-inspired many-objective diet problem,” Health Informatics Journal, vol. 27, no. 1, p. 1460458220976719, 2021.
E. M. Porras, A. C. Fajardo, and R. P. Medina, “Solving dietary planning problem using particle swarm optimization with genetic operators,” in Proceedings of the 3rd international conference on machine learning and soft computing, 2019, pp. 55–59.
P. Ducrot, C. Méjean, V. Aroumougame, G. Ibanez, B. Allès, E. Kesse-Guyot, S. Hercberg, and S. Péneau, “Meal planning is associated with food variety, diet quality and body weight status in a large sample of french adults,” International journal of behavioral nutrition and physical activity, vol. 14, pp. 1–12, 2017.
W. D. H. Wijekoon and S. Harshanath, “Meal preparation algorithm for diabetic patients using machine learning,” Sri Lankan Journal of Applied Sciences, vol. 1, no. 02, pp. 27–33, 2023.
J. Who and F. E. Consultation, “Diet, nutrition and the prevention of chronic diseases,” World Health Organ Tech Rep Ser, vol. 916, no. i-viii, pp. 1–149, 2003.
S. C. on the Scientific Evaluation of Dietary Reference Intakes, S. on Interpretation, U. of Dietary Reference Intakes, S. on Upper Reference Levels of Nutrients, P. on the Definition of Dietary Fiber, and P. on Macronutrients, Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. National Academies Press, 2005.
Publicado
22/10/2025
Como Citar
SILVA, Thalyson G. N. da; CAMPOS, Gustavo A. L. de; AMARO JÚNIOR, Bonfim; BARROS, Ana Luiza B. de Paula.
A Bio-Inspired AI Approach to Personalized Dietary Planning for Chronic Disease Prevention. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 194-201.
DOI: https://doi.org/10.5753/latinoware.2025.16268.
