MODELING HYPERTENSION DISEASE RISK IN INDONESIA USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND BINARY LOGISTIC REGRESSION APPROACHES

  • Nur Chamidah Mathematics Department, Faculty of Science and Technology, Universitas Airlangga, Indonesia https://orcid.org/0000-0003-1592-4671
  • Ardana Tegar Hendrawan Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Figo Surya Ardiyanto Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Martha Sayyida Hammami Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Nurul Izzah Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Salsabila Niken Hariadi Statistics Study Program, Faculty of Science and Technology, Universitas Airlangga, Indonesia
DOI: https://doi.org/10.30598/barekengvol18iss4pp2217-2230
Keywords: Hypertension, Regression, MARS, Binary Logit Regression

Abstract

In the pursuit of the Sustainable Development Goals (SDGs), health-related challenges, especially hypertension, remain a significant global issue. The third goal of the SDGs aims to improve the quality of life and well-being of all individuals, but hypertension is a serious problem that can hinder these goals. Often referred to as the "silent killer" by the World Health Organization (WHO), hypertension is exacerbated by low awareness. Globally, more than 1.28 billion adults suffer from hypertension, with most cases in lower to middle-income countries, including Indonesia. Indonesia has an alarming rate of hypertension incidence, ranking fifth highest in the world. Riset Kesehatan Dasar (Riskesdas) 2023 and the Indonesia Family Life Survey (IFLS) are critical for understanding hypertension risk factors in Indonesia. The IFLS data, obtained from www.rand.org, includes observations from October 2014 to April 2015, totalling 85 observations. Despite being over 10 years old, this dataset was selected because it remains the most recent comprehensive data available from RAND, representing 83% of the Indonesian population. The IFLS is conducted every 7-8 years, with the next wave of data expected soon. Most studies on hypertension globally and in Indonesia use parametric regression methods. However, a research gap exists as no studies have used Multivariate Adaptive Regression Splines (MARS) on IFLS data to analyze hypertension risk factors. This study addresses this gap by comparing binary logit regression and MARS. The analysis shows the Apparent Error Rate (APPER) for MARS is 84.706%, while for binary logistic regression it is 80%, indicating MARS is better at classifying hypertension data in Indonesia. Using MARS offers a novel approach to understanding hypertension risk factors in Indonesia. Despite the data's age, it remains relevant as primary causes and risk factors for hypertension have not changed, making the findings valuable for current health policy and strategies.

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Published
2024-10-11
How to Cite
[1]
N. Chamidah, A. Hendrawan, F. Ardiyanto, M. Hammami, N. Izzah, and S. Hariadi, “MODELING HYPERTENSION DISEASE RISK IN INDONESIA USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND BINARY LOGISTIC REGRESSION APPROACHES”, BAREKENG: J. Math. & App., vol. 18, no. 4, pp. 2217-2230, Oct. 2024.
Issue
Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2024 Nur Chamidah, Ardana Tegar Hendrawan, Figo Surya Ardiyanto, Martha Sayyida Hammami, Nurul Izzah, Salsabila Niken Hariadi

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