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Hypertension prediction using machine learning algorithm among Indonesian adults

IAES International Journal of Artificial Intelligence · Vol. 12 No. 2 (2023) · hlm. 776–784

DOI
10.11591/ijai.v12.i2.pp776-784
ISSN
2252-8938, 2089-4872
Terbit
2023-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.

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  • University of Indonesia
  • Ministry of Population and Family Planning
  • Universitas Muhammadiyah Jakarta
  • Gunadarma University
  • Universitas Binawan
  • University of Nusa Cendana
  • Institut Teknologi Sepuluh Nopember
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Krisna YettiUniversity of Indonesia3
Evi MarthaUniversity of Indonesia2
Welcy FineAfiliasi tidak diketahui2
Hafsah Farah FadhilahAfiliasi tidak diketahui2
Siti FathonahState University of Semarang2
Lina WidyastutiMinistry of Population and Family Planning2
Sudibyo AlimoesoMinistry of Population and Family Planning2
Meindy DiainiMinistry of Population and Family Planning2
Muhammad KodirAfiliasi tidak diketahui2
R. Ahmad Imanullah ZakariyaUniversity of Indonesia1
Kuni InayahAfiliasi tidak diketahui1
Kuni InayahUniversity of Indonesia1
Sefiulki Weni SariAfiliasi tidak diketahui1
Sefiulki Weni SariUniversity of Indonesia1
Wirawan WirawanInstitut Teknologi Sepuluh Nopember1
Kalvein RanteloboUniversity of Nusa Cendana1
Anggi AnitiaUniversity of Indonesia1
Dumilah AyuningtyasUniversity of Indonesia1
Muhammad Sapoan HadiUniversity of Indonesia1
Sutanto Priyo HastonoUniversity of Indonesia1
Lea Morry Br GintingUniversity of Indonesia1
Cindy Nur KhalizaUniversity of Indonesia1
Iwan AriawanUniversity of Indonesia1
Herlina Jamaluddin EL- MaturyInstitut Kesehatan Deli Husada Deli Tua1
Dinda Iryawati Bedy SaskitoAfiliasi tidak diketahui1
Budi HartonoUniversitas Muhammadiyah Jakarta1
Muhammad FachriUniversitas Muhammadiyah Jakarta1
STR Tri HandayaniUniversitas Muhammadiyah Jakarta1
Ratna DjuwitaUniversity of Indonesia1
Cici Apriza YantiUniversitas Prima Nusantara Bukittinggi1
Sri Novita LubisUniversitas Sumatera Utara1
Sudarto RonoatmodjoUniversity of Indonesia1
Ryza Jazid Baharuddin NurHasanuddin University1

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) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Hypertension prediction using machine learning algorithm among Indonesian adults</dc:title>
	<dc:creator>Kurniawan, Rico</dc:creator>
	<dc:creator>Utomo, Budi</dc:creator>
	<dc:creator>Siregar, Kemal N.</dc:creator>
	<dc:creator>Ramli, Kalamullah</dc:creator>
	<dc:creator>Besral, Besral</dc:creator>
	<dc:creator>Suhatril, Ruddy J.</dc:creator>
	<dc:creator>Pratiwi, Okky Assetya</dc:creator>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:subject xml:lang="en-US">Hypertension; Machine learning; Prediction model</dc:subject>
	<dc:description xml:lang="en-US">Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21625</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i2.pp776-784</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 2: June 2023; 776-784</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21625/13643</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/21625/2951</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>