Estimativa dos Níveis de Obesidade com Base em Hábitos Alimentares e Condição Física Através de Técnicas de Aprendizado de Máquina
Abstract
Obesity is a chronic disease that affects several countries, causing damage such as respiratory and locomotor difficulties, metabolic changes, cardiovascular problems, and even death, in the extreme case. In this perspective, this initial study aims to evaluate the classifiers' performance, namely, Random Forest and Support Vector Machine, when estimating obesity levels, with data from the set "Estimation of obesity levels based on eating habits and physical condition Data Set". Under cross-validation and Hold-Out, preliminary results indicate an average accuracy with SVM around 87.84% and RF around 95.18%. Furthermore, we noticed that our approach recognizes overweight and obesity cases better, while such cases, in the latest work, are more critically neglected, misclassifying the most severe degree of obesity. Thus, comparing our results with related works, we concluded that the models studied are suitable to the problem, given the achieved results.References
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A. R. d. O. Pinheiro, S. F. T. d. Freitas, and A. C. T. Corso, “Uma abordagem epidemiológica da obesidade,” Revista de Nutrição, vol. 17, no. 4, pp. 523–533, 2004.
E. C. C. d. S. Bulsing et al., “Asma, índice de massa corporal e sintomas respiratórios: um estudo de base populacional,” 2019.
E. N. Wanderley and V. A. Ferreira, “Obesidade: uma perspectiva plural,” Ciência & Saúde Coletiva, vol. 15, pp. 185–194, 2010.
C. N. Freitas, F. R. Cordeiro, and V. Macario, “Myfood: A food segmentation and classification system to aid nutritional monitoring,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020, pp. 234–239.
A. M. da Rocha Fernandes, M. B. Pinheiro, and A. G. Nazário, “Aplicação de Algoritmos de Aprendizado de Máquina no Apoio a Elaboração de Planos Nutricionais,” Brazilian Journal of Development, vol. 6, no. 8, pp. 60 935–60 944, 2020.
A. De la Hoz Manotas, E. De la Hoz Correa, F. Mendoza, R. Morales, and B. Sanchez, “Obesity Level Estimation Software based on Decision Trees,” Journal of Computer Science, vol. 15, p. 10, 01 2019.
F. M. Palechor and A. de la Hoz Manotas, “Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico,” Data in Brief, vol. 25, p. 104344, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352340919306985
A. S. Selya and D. Anshutz, “Machine learning for the classification of obesity from dietary and physical activity patterns,” in Advanced Data Analytics in Health. Springer, 2018, pp. 77–97.
K. Jindal, N. Baliyan, and P. S. Rana, “Obesity prediction using ensemble machine learning approaches,” in Recent Findings in Intelligent Computing Techniques. Springer, 2018, pp. 355–362.
M. A. H. e. C. J. P. Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques,” 2016. [Online]. Available: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf
Published
2021-10-18
How to Cite
LOPES, Leonardo Ferreira; OLIVEIRA, Adonias Caetano de; BRITO, Rhyan Ximenes de; OLIVEIRA, Saulo Anderson Freitas de; RAPOSO NETO, Luiz Torres.
Estimativa dos Níveis de Obesidade com Base em Hábitos Alimentares e Condição Física Através de Técnicas de Aprendizado de Máquina. In: WORKSHOP OF WORKS IN PROGRESS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 154-157.
DOI: https://doi.org/10.5753/sibgrapi.est.2021.20029.
