Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques

Overweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions. © 2023 Elsevier B.V.. All rights reserved.

Notas

Indexación: Scopus.

Palabras clave

Biochemical profiles, Classification, Lipid profiles, Machine learning, Normal-weight, Obesity, Overweight

Citación

Procedia Computer Science. Volume 220, Pages 978 - 983. 2023 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023. Leuven. 15 March 2023through 17 March 2023. Code 189712

DOI

10.1016/j.procs.2023.03.135

URI

https://repositorio.unab.cl/handle/ria/56054

Colecciones

FIng - Artículos de Revista

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