TRADITIONAL LOGISTIC REGRESSION AND MACHINE LEARNING APPROACHES OF SOCIODEMOGRAPHIC AND ANTHROPOMETRIC FACTORS INFLUENCING HYPERTENSION IN ATHLETES

  • A'yunin Sofro Department of Actuarial Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, Indonesia https://orcid.org/0000-0003-2603-4092
  • Asri Maharani Mental Health Research Group, Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester and Manchester Academic Health Science Centre (MAHSC) Oxford Rd, United Kingdom https://orcid.org/0000-0002-5931-8692
  • Mutia Eva Mustafidah Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, Indonesia https://orcid.org/0009-0007-3560-4890
  • Khusnia Nurul Khikmah Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Palangka Raya, Indonesia https://orcid.org/0000-0002-9142-6968
  • Affiati Oktaviarina Department of Actuarial Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, Indonesia https://orcid.org/0009-0002-6564-7879
  • Danang Ariyanto Department of Actuarial Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, Indonesia https://orcid.org/0000-0002-7642-6775
DOI: https://doi.org/10.30598/barekengvol20iss2pp1125–1138
Keywords: Athletes, Binary logistic regression, Hypertension, Machine learning, Random effects

Abstract

The type and intensity of exercise performed by athletes play an important role in affecting blood pressure stability, putting them at risk of developing hypertension. Hypertension, or high blood pressure, is a medical condition in which the blood pressure in the arteries rises above normal limits. Hypertension in athletes becomes an essential factor in real cases if not detected early. Therefore, this study aims to model and analyse the sociodemographic and anthropometric factors that influence the incidence of hypertension. The data used in this study are primary data from 200 athlete selection participants at the University of Surabaya and the Indonesian National Sports Committee (INSC) of East Java. This research method proposes to compare the traditional approach with machine learning to prove the accuracy comparison of the model's goodness, where both approaches are proposed by considering the novelty proposed through the machine learning approach but still maximizing the traditional approach. The proposed methods are binary logistic regression, binary logistic regression with the addition of random effects, highly randomized tree, and support vector classification. The binary logistic regression model is better than the binary logistic regression model with random effects, random trees, and support vector classification because the accuracy, sensitivity, specificity, and F1-score value (68.5%, 69%, 68%, and 68.8%) is highest than the others. Other results showed that the waist circumference variable, the father's occupation variable, and the salary variable significantly affected hypertension at the 5% significance level.

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Published
2026-01-26
How to Cite
[1]
A. Sofro, A. Maharani, M. E. Mustafidah, K. N. Khikmah, A. Oktaviarina, and D. Ariyanto, “TRADITIONAL LOGISTIC REGRESSION AND MACHINE LEARNING APPROACHES OF SOCIODEMOGRAPHIC AND ANTHROPOMETRIC FACTORS INFLUENCING HYPERTENSION IN ATHLETES”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1125–1138, Jan. 2026.
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Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2026 A'yunin Sofro, Asri Maharani, Mutia Eva Mustafidah, Khusnia Nurul Khikmah, Affiati Oktaviarina, Danang Ariyanto

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