Application of Machine Learning Methods for Nutritional Assessment in the Seridó/RN Region
DOI:
https://doi.org/10.5753/reic.2026.7207Keywords:
Data Mining, Machine Learning, Algorithms, Clustering, Association Rules, Nutrition, Dietary PatternsAbstract
The identification of dietary patterns in nutrition has incorporated data mining to analyze large volumes of information. This exploratory study analyzed 407 inhabitants of the Seridó region to investigate fruit consumption patterns and their association with sociodemographic, anthropometric, and personal preference variables. The method involved structured forms, data pre-processing, and the use of k-means and Apriori algorithms to identify clusters and association rules. The results revealed three hierarchical consumption patterns influenced by age and consumption times, in which the groups showed progressively higher fruit ratings. The consumption of stone fruits (plum, cherry, peach, and persimmon) was strongly associated with the acceptance of other fruit groups. It is concluded that age, personal preferences, and consumption times are predictors of diets with greater fruit variety within the studied population, providing a theoretical basis for regionalized nutritional analyses.
Downloads
References
Alpaydin, E. (2020). Introduction to machine learning. Fourth. MIT Press.
Alves, M. d. A., Souza, A. d. M., Barufaldi, L. A., Tavares, B. M., Bloch, K. V., and de Vasconcelos, F. d. A. G. d. (2019). Dietary patterns of brazilian adolescents according to geographic region: an analysis of the study of cardiovascular risk in adolescents (erica). Cadernos de Saúde Pública. DOI: 10.1590/0102-311X00153818.
Botelho, R. B. A. (2006). Culinária regional: o nordeste e a alimentação saudável. Brasília: Universidade de Brasília. Disponível em: [link].
BRASIL, M. d. S. (2020). Protocolo clínico e diretrizes terapêuticas de sobrepeso e obesidade em adultos. DF: Ministério da Saúde/Conitec. Disponível em: [link]. Acesso em: 9 out. 2025.
Carvalho, A., Faceli, K., Lorena, A., and Gama, J. (2011). Inteligência Artificial–uma abordagem de aprendizado de máquina, volume 2. Livros Técnicos e Científicos Editora Ltda.
Castro, L. N. d. and Ferrari, D. G. (2016). Introdução à mineração de dados: conceitos básicos, algoritmos e aplicações, volume 5. Saraiva Uni.
Chika, P., Relita, B., and Sitompul, M. P. (2024). Application of apriori algorithm in determining behavioral patterns and lifestyle of gerd patients. International Journal of Informatics, Economics, Management and Science, 3(2):146–160. DOI: 10.52362/ijiems.v3i2.1593.
Dangeti, P. (2017). Statistics for machine learning. Packt Publishing Ltd, Birmingham, U.K.
Donatti, T., Henn, R. L., and Cremonese, C. (2023). Dietary patterns of elementary school students in southern brazil and associated factors: a cross-sectional school-based study. Braz J Devel. DOI: 10.34117/bjdv9n1-052.
Enthoven, L. and Van den Broeck, G. (2021). Local food systems: Reviewing two decades of research. Agricultural systems, 193:103226. DOI: 10.1016/j.agsy.2021.103226.
Hackeling, G. (2014). Mastering Machine Learning with scikit-learn. Packt Publishing Ltd, Birmingham, U.K.
Lazarou, C., Karaolis, M., Matalas, A.-L., and Panagiotakos, D. B. (2012). Dietary patterns analysis using data mining method. an application to data from the cykids study. Computer Methods and Programs in Biomedicine, 108(2):706–714. DOI: 10.1016/j.cmpb.2011.12.011.
Martins, I. C. V., Hardman, C. M., de Souza Andrade, M. L. S., dos Santos, A. R. M., Brito, A. L. S., Soares, F. C., and de Barros, M. V. G. (2020). Diferenças regionais do consumo de frutas, verduras e hortaliças em adolescentes. RBONE-Revista Brasileira de Obesidade, Nutrição e Emagrecimento, 14(88):906–913. Disponível em: [link].
Martins, M. L. B., Tonial, S. R., Gama, M. E. A., Ribeiro, T. H., Ribeiro, J. M., Barbosa, J. M. A., et al. (2014). Consumo de alimentos entre adolescentes de um estado do nordeste brasileiro. DEMETRA: Alimentação, Nutrição & Saúde, 9(2):577–594. DOI: 10.12957/demetra.2014.9693.
Mewes, I. R., Jenzer, H., and Einsele, F. (2021). A study about discovery of critical food consumption patterns linked with lifestyle diseases for swiss population using data mining methods. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - HEALTHINF, pages 30–38. INSTICC, SciTePress. DOI: 10.5220/0010160200300038.
Niño-Adan, I., Landa-Torres, I., Portillo, E., and Manjarres, D. (2022). Influence of statistical feature normalisation methods on k-nearest neighbours and k-means in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 111:104807. DOI: 10.1016/j.engappai.2022.104807.
Olutunde, T., Ani, C. L., and Adesue, G. A. (2024). Leveraging machine learning for personalized dietary recommendations, nutritional patterns, and health outcome predictions. Journal of Science Research and Reviews, 1(2):43–56. DOI: 10.70882/josrar.2024.v1i2.40.
Ornellas, L. H. (2007). Técnica dietética: seleção e preparo de alimentos. Atheneu Editora São Paulo Ltda., 8th edition.
Philippi, S. T. (2014). Nutrição e técnica dietética. Editora Manole Ltda., 3th edition.
Shao, A., Drewnowski, A., Willcox, D., Krämer, L., Lausted, C., Eggersdorfer, M., Mathers, J., Bell, J., Randolph, R., Witkamp, R., et al. (2017). Optimal nutrition and the ever-changing dietary landscape: a conference report. European journal of nutrition, 56(1):1–21. DOI: 10.1007/s00394-017-1460-9.
Silva, L. A. d., Peres, S. M., and Boscarioli, C. (2017). Introdução à mineração de dados: com aplicações em R. Elsevier Brasil.
Silva, V. C., Gorgulho, B., Marchioni, D. M., Araujo, T. A. d., Santos, I. d. S., Lotufo, P. A., and Benseñor, I. M. (2022). Clustering analysis and machine learning algorithms in the prediction of dietary patterns: Cross-sectional results of the brazilian longitudinal study of adult health (elsa-brasil). Journal of Human Nutrition and Dietetics, 35(5):883–894. DOI: 10.1111/jhn.12992.
Souza, R. S. d., Arbage, A. P., Neumann, P. S., Froehlich, J. M., Diesel, V., Silveira, P. R., Silva, A. d., Corazza, C., Baumhardt, E., and Lisboa, R. d. S. (2008). Comportamento de compra dos consumidores de frutas, legumes e verduras na região central do rio grande do sul. Ciência Rural, 38(2):511–517. DOI: 10.1590/S0103-84782008000200034.
Sullivan, G. M. and Artino Jr, A. R. (2013). Analyzing and interpreting data from likert-type scales. Journal of graduate medical education, 5(4):541. DOI: 10.4300/JGME-5-4-18.
WHO Working Group (1986). Use and interpretation of anthropometric indicators of nutritional status. Bulletin of the World Health Organization, 64(6):929–941. Disponível em: [link].
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 The authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
