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

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 4 (2024) · hlm. 2217–2230

DOI
10.30598/barekengvol18iss4pp2217-2230
ISSN
2615-3017, 1978-7227
Terbit
2024-10-11
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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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  • Universitas Airlangga
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Toha SaifudinUniversitas Airlangga7
Juan Krisfigo PrasetyoAfiliasi tidak diketahui2
Berliani LarasatiAfiliasi tidak diketahui2
Bagas Shata PratamaAfiliasi tidak diketahui2
Dita AmeliaAfiliasi tidak diketahui2
Rien RecyliaAfiliasi tidak diketahui2
Belindha Ayu ArdhaniUniversitas Airlangga1
Nia SaurinaUniversitas Wijaya Kusuma Surabaya1
Riries RulaningtyasUniversitas Airlangga1
Aryati AryatiUniversitas Airlangga1
Rizky Ismaul Uyun HidayatAfiliasi tidak diketahui1
Mutiara AisharezkaAfiliasi tidak diketahui1
Naufal Ramadhan Al Akhwal SiregarAfiliasi tidak diketahui1
Muhammad Fikry Al FariziAfiliasi tidak diketahui1
Atikah FaizaAfiliasi tidak diketahui1
Muhammad Hilmi FibryanAfiliasi tidak diketahui1
Alda Fuadiyah SuryonoAfiliasi tidak diketahui1
Nina AuliyahAfiliasi tidak diketahui1
Alfi Nur NitasariAfiliasi tidak diketahui1
Andini Sa'idahAfiliasi tidak diketahui1
Nurin FaizunAfiliasi tidak diketahui1
Kezia Eunike DarmawanAfiliasi tidak diketahui1
Marfa Audilla FitriAfiliasi tidak diketahui1
Muhammad Althof JuniarAfiliasi tidak diketahui1
Azzahra FaniaAfiliasi tidak diketahui1
Diana UlyaAfiliasi tidak diketahui1
Rico RamadhanAfiliasi tidak diketahui1
Nadhira Safa KamiilahAfiliasi tidak diketahui1
Putu Eka AndrianiAfiliasi tidak diketahui1
Cynthia Anggelyn SiburianAfiliasi tidak diketahui1
Muhammad Rizaldy BaihaqiAfiliasi tidak diketahui1
Salma Bethari Andjani SAfiliasi tidak diketahui1
Alma Khalisa RahmaAfiliasi tidak diketahui1
Qumadha Zaenal AbidinAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">MODELING HYPERTENSION DISEASE RISK IN INDONESIA USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND BINARY LOGISTIC REGRESSION APPROACHES</dc:title>
	<dc:creator>Chamidah, Nur</dc:creator>
	<dc:creator>Hendrawan, Ardana Tegar</dc:creator>
	<dc:creator>Ardiyanto, Figo Surya</dc:creator>
	<dc:creator>Hammami, Martha Sayyida</dc:creator>
	<dc:creator>Izzah, Nurul</dc:creator>
	<dc:creator>Hariadi, Salsabila Niken</dc:creator>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-10-11</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12241</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss4pp2217-2230</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application; 2217-2230</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss4year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12241/8897</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Nur Chamidah, Ardana Tegar Hendrawan, Figo Surya Ardiyanto, Martha Sayyida Hammami, Nurul Izzah, Salsabila Niken Hariadi</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>