International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Pineda-Rico Zaira 1*, Rojas Mendoza Diana Luz de los Angeles 1, Pineda-Rico Ulises 2, Arguelles Ojeda Jose Luis 1, Martinez Lopez Francisco Javier 1

1 Coordinacion Academica Region Altiplano, Universidad Autonoma de San Luis Potosi, Matehuala, S.L.P, 78700, Mexico

2 Facultad de Ciencias, Universidad Autonoma de San Luis Potosi., Privadas del Pedregal, 78295, Mexico

 

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ABSTRACT

The detection of individuals with obesity or overweight allows to predict the prevalence of health risks, such as premature death, disabilities and other chronic diseases. This study describes a pilot conducted on the members of a higher education staff in the city of Matehuala, Mexico. It involved processing anthropometric measurements, health indicators and the results of bioelectrical impedance analysis using machine learning techniques. The goal was to identify the metabolic aging of individuals. The recorded data were used to create a database that was subsequently employed in four different classification models: decision tree, random forest, artificial neural networks and adaptive boosting. Additionally, four statistical techniques were utilized to determine variable importance scores: Pearson, Chi2, Anova, recursive elimination method and the variance inflation factor. The variable importance score was employed to identify the features that were most consistently repeated across methods. This analysis concluded that both anthropometric measurements and the results of bioelectrical impedance analysis provide valuable references for identifying obesity and overweight in individuals. Among the anthropometric measurements that exhibited a greater impact on the models' predictions were waist-to-height ratio, hip and arm circumferences, body mass index, systolic and diastolic blood pressure and heart rate. Additionally, body fat and muscle mass also contributed significantly.


Keywords: Classification, Machine learning, Obesity prediction, Variable importance.


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ARTICLE INFORMATION

Received: 2023-07-18
Revised: 2023-08-25
Accepted: 2023-09-18
Available Online: 2023-12-01


Cite this article:

Zaira, P.-R., de los Angeles, R.M.D.L., Ulises, P.-R., Luis, A.O.J., Javier, M.L.F. 2023. Prediction of metabolic ageing in higher education staff using machine learning: A pilot study. International Journal of Applied Science and Engineering, 20, 2023247. https://doi.org/10.6703/IJASE.202312_20(4).009

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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