Akses terbuka Jurnal SINTA 3
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
- ISSN
- 2615-3017, 1978-7227
- Terbit
- 2024-10-11
- Penerbit
- PATTIMURA UNIVERSITY
- Bahasa
- eng
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
- Universitas Wijaya Kusuma Surabaya
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- Organisasi berbeda
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Tampilkan sebagai tabel
| Rekan penulis | Organisasi | Artikel bersama |
|---|---|---|
| Toha Saifudin | Universitas Airlangga | 7 |
| Juan Krisfigo Prasetyo | Afiliasi tidak diketahui | 2 |
| Berliani Larasati | Afiliasi tidak diketahui | 2 |
| Bagas Shata Pratama | Afiliasi tidak diketahui | 2 |
| Dita Amelia | Afiliasi tidak diketahui | 2 |
| Rien Recylia | Afiliasi tidak diketahui | 2 |
| Belindha Ayu Ardhani | Universitas Airlangga | 1 |
| Nia Saurina | Universitas Wijaya Kusuma Surabaya | 1 |
| Riries Rulaningtyas | Universitas Airlangga | 1 |
| Aryati Aryati | Universitas Airlangga | 1 |
| Rizky Ismaul Uyun Hidayat | Afiliasi tidak diketahui | 1 |
| Mutiara Aisharezka | Afiliasi tidak diketahui | 1 |
| Naufal Ramadhan Al Akhwal Siregar | Afiliasi tidak diketahui | 1 |
| Muhammad Fikry Al Farizi | Afiliasi tidak diketahui | 1 |
| Atikah Faiza | Afiliasi tidak diketahui | 1 |
| Muhammad Hilmi Fibryan | Afiliasi tidak diketahui | 1 |
| Alda Fuadiyah Suryono | Afiliasi tidak diketahui | 1 |
| Nina Auliyah | Afiliasi tidak diketahui | 1 |
| Alfi Nur Nitasari | Afiliasi tidak diketahui | 1 |
| Andini Sa'idah | Afiliasi tidak diketahui | 1 |
| Nurin Faizun | Afiliasi tidak diketahui | 1 |
| Kezia Eunike Darmawan | Afiliasi tidak diketahui | 1 |
| Marfa Audilla Fitri | Afiliasi tidak diketahui | 1 |
| Muhammad Althof Juniar | Afiliasi tidak diketahui | 1 |
| Azzahra Fania | Afiliasi tidak diketahui | 1 |
| Diana Ulya | Afiliasi tidak diketahui | 1 |
| Rico Ramadhan | Afiliasi tidak diketahui | 1 |
| Nadhira Safa Kamiilah | Afiliasi tidak diketahui | 1 |
| Putu Eka Andriani | Afiliasi tidak diketahui | 1 |
| Cynthia Anggelyn Siburian | Afiliasi tidak diketahui | 1 |
| Muhammad Rizaldy Baihaqi | Afiliasi tidak diketahui | 1 |
| Salma Bethari Andjani S | Afiliasi tidak diketahui | 1 |
| Alma Khalisa Rahma | Afiliasi tidak diketahui | 1 |
| Qumadha Zaenal Abidin | Afiliasi tidak diketahui | 1 |
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