Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method

Hypertension is one of the important public health problems in Indonesia, which contributes to the high prevalence of non-communicable diseases. This study aims to model the prevalence of hypertension in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method to identify significant predictors and their interactions. The data used was secondary data from the 2023 Indonesian Health Survey, including variables such as smoking prevalence, physical inactivity, dietary habits (consumption of fatty and sweet foods), lack of fruit and vegetable consumption, and obesity prevalence. The MARS method was used to analyse the nonlinear relationships and interactions between these predictors. After a trial-and-error process to determine the optimal number of basis functions (BF), maximum interactions (MI), and minimum observations (MO), the best model was achieved with BF = 18, MI = 3, and MO = 1. This model produced a Generalised Cross Validation (GCV) value of 13.428 and R-Square of 0.278. This fairly low R-Square value indicates that the factors analysed have contributed to the variation in hypertension prevalence, but there are still other aspects that can be taken into account to improve the predictive power of the model. The significant predictor variables were consumption of fatty foods (X3), lack of physical activity (X2), and consumption of sweets (X4), with the highest importance on X3 (100%). The findings reveal that interactions between variables, such as dietary habits and physical inactivity, significantly influence the prevalence of hypertension. For example, higher consumption of fatty and sweet foods combined with low physical activity increases the risk of hypertension. These results demonstrate the effectiveness of the MARS method in capturing complex and nonlinear relationships and serve as findings that highlight the need for health policies that focus on healthy diets and increased physical activity, in line with Goal 3 of the SDGs, “Good Health and Well-Being,” which aims to reduce premature mortality from noncommunicable diseases. Recommended interventions include nutrition education campaigns and community-based exercise programs to reduce the prevalence of hypertension in Indonesia.

Authors

Publication Details

Journal
JTAM (Jurnal Teori dan Aplikasi Matematika)
Published
2025-04-25
DOI
https://doi.org/10.31764/jtam.v9i2.28392
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method

Suliyanto Suliyanto, Toha Saifudin, Niswa Faizah Aulia, Sheila Sevira Asteriska Naura et al.
JTAM (Jurnal Teori dan Aplikasi Matematika)
Data Mining and Machine Learning Applications
article

Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method

Suliyanto Suliyanto, Toha Saifudin, Niswa Faizah Aulia, Sheila Sevira Asteriska Naura, Sanda Insania Dewanty, Indana Zulfa Wulandari, Nabila Shafa Aflaha
article en

Abstract

Hypertension is one of the important public health problems in Indonesia, which contributes to the high prevalence of non-communicable diseases. This study aims to model the prevalence of hypertension in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method to identify significant predictors and their interactions. The data used was secondary data from the 2023 Indonesian Health Survey, including variables such as smoking prevalence, physical inactivity, dietary habits (consumption of fatty and sweet foods), lack of fruit and vegetable consumption, and obesity prevalence. The MARS method was used to analyse the nonlinear relationships and interactions between these predictors. After a trial-and-error process to determine the optimal number of basis functions (BF), maximum interactions (MI), and minimum observations (MO), the best model was achieved with BF = 18, MI = 3, and MO = 1. This model produced a Generalised Cross Validation (GCV) value of 13.428 and R-Square of 0.278. This fairly low R-Square value indicates that the factors analysed have contributed to the variation in hypertension prevalence, but there are still other aspects that can be taken into account to improve the predictive power of the model. The significant predictor variables were consumption of fatty foods (X3), lack of physical activity (X2), and consumption of sweets (X4), with the highest importance on X3 (100%). The findings reveal that interactions between variables, such as dietary habits and physical inactivity, significantly influence the prevalence of hypertension. For example, higher consumption of fatty and sweet foods combined with low physical activity increases the risk of hypertension. These results demonstrate the effectiveness of the MARS method in capturing complex and nonlinear relationships and serve as findings that highlight the need for health policies that focus on healthy diets and increased physical activity, in line with Goal 3 of the SDGs, “Good Health and Well-Being,” which aims to reduce premature mortality from noncommunicable diseases. Recommended interventions include nutrition education campaigns and community-based exercise programs to reduce the prevalence of hypertension in Indonesia.

JTAM (Jurnal Teori dan Aplikasi Matematika)Vol. 9(2)
Openalex Percentile: Top 64%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.