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

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

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.

Keywords


Hypertension; MARS; Risk Factors; Public Health; SDGs.

Full Text:

DOWNLOAD [PDF]

References


Addini, P. F., Hadi, W., Muslim, P., & Harahap, R. (2023). Application Of The Multivariate Adaptive Regression Spline (Mars) Method In Analyzing Misclassification Of Elementary School Accreditation Data In The City Of Tebing Tinggi. Jurnal Scientia, 12(1), 617–620. http://infor.seaninstitute.org/index.php

Andini, F. A. D., & Siregar, A. Y. M. (2024). Work hours and the risk of hypertension: the case of Indonesia. BMC Public Health, 24(1), 2480. https://doi.org/10.1186/s12889-024-20003-z

Badan Kebijakan Pembangunan Kesehatan. (2023). Survei Kesehatan Indonesia Dalam Angka.

Bekar Adiguzel, M., & Cengiz, M. A. (2023). Model selection in multivariate adaptive regressions splines (MARS) using alternative information criteria. Heliyon, 9(9), e19964. https://doi.org/10.1016/j.heliyon.2023.e19964

Casas, R., Castro-Barquero, S., Estruch, R., & Sacanella, E. (2018). Nutrition and cardiovascular health. In International Journal of Molecular Sciences (Vol. 19, Issue 12). MDPI AG. https://doi.org/10.3390/ijms19123988

Chang, C. C., Yeh, J. H., Chiu, H. C., Liu, T. C., Chen, Y. M., Jhou, M. J., & Lu, C. J. (2023). Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach. Frontiers in Neurology, 14, 1283214. https://doi.org/https://doi.org/10.3389/fneur.2023.1283214

Forouhi, N. G., Krauss, R. M., Taubes, G., & Willett, W. (2018). Dietary fat and cardiometabolic health: Evidence, controversies, and consensus for guidance. BMJ (Online), 361, 1–8. https://doi.org/10.1136/bmj.k2139

Friedman, J. H. (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1 – 67. https://doi.org/10.1214/aos/1176347963

Friedman, J. H., & Roosen, C. B. (1995). An introduction to multivariate adaptive regression splines. Statistical Methods in Medical Research, 4(3), 197–217. https://doi.org/10.1177/096228029500400303

Junaedi, M., Nasikhin, Hasanah, S., & Hassan, Z. (2023). Learning Patterns in Influencing Attitudes of Religious Tolerance in Indonesian Universities. Education Sciences, 13(3), 285. https://doi.org/10.3390/educsci13030285

López, F., & Kholodilin, K. (2023). Putting MARS into space. Non-linearities and spatial effects in hedonic models. Papers in Regional Science, 102(4), 871–896. https://doi.org/10.1111/pirs.12738

Maleki, J., & Pak, A. (2022). A Rapid Design Procedure for Tied-Back Soil Walls Using Multivariate Adaptive Regression Splines (MARS) Method. Geotechnical and Geological Engineering, 41(2), 1521–1535. https://doi.org/10.1007/s10706-022-02351-y

Maniero, C., Lopuszko, A., Papalois, K. B., Gupta, A., Kapil, V., & Khanji, M. Y. (2023). Non-pharmacological factors for hypertension management: a systematic review of international guidelines. In European Journal of Preventive Cardiology (Vol. 30, Issue 1, pp. 17–33). Oxford University Press. https://doi.org/10.1093/eurjpc/zwac163

Mutebi, R. K., Semulimi, A. W., Mukisa, J., Namusobya, M., Namirembe, J. C., Nalugga, E. A., Batte, C., Mukunya, D., Kirenga, B., Kalyesubula, R., & Byakika-Kibwika, P. (2023). Prevalence of and Factors Associated with Hypertension Among Adults on Dolutegravir-Based Antiretroviral Therapy in Uganda: A Cross Sectional Study. Integrated Blood Pressure Control, 16, 11–21. https://doi.org/10.2147/IBPC.S403023

Ningsih, P. S. (2024). Pemodelan Multivariate Adaptive Regression Splines pada Prevalensi Hipertensi di Provinsi Jawa Timur. Institut Teknologi Sepuluh Nopember.

Özmen, A., Zinchenko, Y., & Weber, G.-W. (2022). Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market. Annals of Operations Research, 324, 1–31. https://doi.org/10.1007/s10479-022-04993-w

Putra, R., Fadhlurrahman, M. G., & Gunardi. (2023). Determination of the best knot and bandwidth in geographically weighted truncated spline nonparametric regression using generalized cross validation. MethodsX, 10, 101994. https://doi.org/10.1016/j.mex.2022.101994

Reynolds, A., Mann, J., Cummings, J., Winter, N., Mete, E., & Te Morenga, L. (2019). Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. The Lancet, 393(10170), 434–445. https://doi.org/10.1016/S0140-6736(18)31809-9

Rodriguez-Clare, A., & Dingel, J. (2021). The Effect Of Compensation, Leadership Style And Work Discipline On The Performance Of Hospital Employee In United States. 12(1), 33–47. https://medalionjournal.com/

Sahraei, M. A., Duman, H., Çodur, M. Y., & Eyduran, E. (2021). Prediction of transportation energy demand: Multivariate Adaptive Regression Splines. Energy, 224(12), 120090. https://doi.org/https://doi.org/10.1016/j.energy.2021.120090

Teo, K. K., & Rafiq, T. (2021). Cardiovascular Risk Factors and Prevention: A Perspective From Developing Countries. In Canadian Journal of Cardiology (Vol. 37, Issue 5, pp. 733–743). Elsevier Inc. https://doi.org/10.1016/j.cjca.2021.02.009

Van Oort, S., Beulens, J. W. J., Van Ballegooijen, A. J., Grobbee, D. E., & Larsson, S. C. (2020). Association of Cardiovascular Risk Factors and Lifestyle Behaviors with Hypertension: A Mendelian Randomization Study. Hypertension, 76(6), 1971–1979. https://doi.org/10.1161/HYPERTENSIONAHA.120.15761

Vintilă, A.-M., & Dorobanțu, M. (2023). Hypertension in Young Adults. In M. Dorobantu, V. Voicu, G. Grassi, E. Agabiti-Rosei, & G. Mancia (Eds.), Hypertension and Heart Failure: Epidemiology, Mechanisms and Treatment (pp. 459–476). Springer International Publishing. https://doi.org/10.1007/978-3-031-39315-0_29

Virtanen, M., & Kivimaki, M. (2018). Long Working Hours and Risk of Cardiovascular Disease. Current Cardiology Reports, 20(11), 123. https://doi.org/10.1007/s11886-018-1049-9

WHO. (2023). Global report on hypertension The race against a silent killer. https://www.who.int/publications/i/item/9789240081062

Yang, B.-Y., Fan, S., Thiering, E., Seissler, J., Nowak, D., Dong, G.-H., & Heinrich, J. (2020). Ambient air pollution and diabetes: A systematic review and meta-analysis. Environmental Research, 180, 108817. https://doi.org/10.1016/j.envres.2019.108817

Yuan, S., Yu, H. jie, Liu, M. wei, Tang, B. wen, Zhang, J., Gasevic, D., Larsson, S. C., & He, Q. qiang. (2020). Fat Intake and Hypertension Among Adults in China: The Modifying Effects of Fruit and Vegetable Intake. American Journal of Preventive Medicine, 58(2), 294–301. https://doi.org/10.1016/j.amepre.2019.09.004

Zhao, Q., Wu, Q., Zhong, H., Yan, B., Wu, J., & Guo, W. (2024). Association of dietary habits with body mass index and waist circumference, and their interaction effect on hypertension. Medicine (United States), 103(20), E38178. https://doi.org/10.1097/MD.0000000000038178

Zhou, B., Perel, P., Mensah, G., & Ezzati, M. (2021). Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nature Reviews Cardiology, 18(11), 785-802. https://doi.org/10.1038/s41569-021-00559-8




DOI: https://doi.org/10.31764/jtam.v9i2.28392

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Suliyanto, Toha Saifudin, Sheila Sevira Asteriska Naura, Sanda Insania Dewanty, Indana Zulfa Wulandari, Nabila Shafa Aflaha, Niswa Faizah Aulia

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

_______________________________________________

JTAM already indexing:

                     


_______________________________________________

 

Creative Commons License

JTAM (Jurnal Teori dan Aplikasi Matematika) 
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

______________________________________________

_______________________________________________

_______________________________________________ 

JTAM (Jurnal Teori dan Aplikasi Matematika) Editorial Office: