Melola Discovery

← Melola Discovery

Open access Jurnal

Covid-19 Patient Mortality Risk Classification Using Bayesian Binary Logistic Regression

Klasifikasi Risiko Kematian Pasien Covid-19 Menggunakan Regresi Logistik Biner Bayesian (RLBB)

Jurnal Matematika, Statistika dan Komputasi · Vol. 18 No. 1 (2021) · pp. 150–160

DOI
10.20956/j.v18i1.14268
ISSN
2614-8811, 1858-1382
Published
2021-09-02
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

At the end of 2019 the world was shocked by a new disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease is called Covid-19 (Coronavirus Disease). The mortality rate due to disease is increasing every day. In Indonesia as of April 2021, confirmed Covid-19 patients who died reached 42,530 patients, seeing the high mortality rate of Covid-19 patients so it needs to be studied further so that the risk of death of these Covid-19 patients can be minimized. This research utilizing binary logistic regression with Bayesian method parameter estimation. In this study, the predictor variables used were in the form of categories that each category in the predictor variables was assumed to have the same risk of death risk of Covid-19 patients. The results of this study indicate that the number of comorbids has a significant effect on the risk of death of Covid-19 patients, the more the number of comorbids suffered by the patient, the higher the risk of death of the patient. The accuracy of this method in classifying data is 84.68%.

Bahasa Indonesia

Pada akhir tahun 2019 dunia digemparkan dengan penyakit baru yang disebabkan oleh SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). Penyakit yang disebabkan oleh SARS-CoV-2 ini disebut dengan Covid-19 (Coronavirus Disease). Angka kematian akibat penyakit ini kian hari kian bertambah. Di Indonesia sendiri per April 2021 pasien yang terkonfirmasi Covid-19 yang meninggal dunia mencapai 42.530 pasien. Melihat tingginya angka kematian pasien Covid-19 sehingga perlu dikaji lebih lanjut agar upaya pencegahan risiko kematian pasien Covid-19 ini dapat diminimalisir. Penelitian ini menggunakan Regresi Logistik Biner dengan pendugaan parameter metode Bayes dan mengukur ketepatan klasifikasi model tersebut pada data penelitian. Pada penelitian ini variabel prediktor yang digunakan berbentuk kategori sehingga setiap kategori pada variabel prediktor diasumsikan memiliki risiko yang sama terhadap risiko kematian pasien Covid-19. Hasil dari penelitian ini diperoleh bahwa Jumlah Komorbid signifikan mempengaruhi risiko kematian pasien Covid-19, semakin jumlah komorbid yang diderita oleh pasien maka semakin tinggi pula risiko kematian pasien tersebut. Adapun ketepatan metode ini dalam mengklasifikasi data sebesar 84,68 %.

Keywords

Collaboration network of the authors

23 authors · 52 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Muhammadiyah Madiun
  • Muhammadiyah University of Surakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Maiyastri MaiyastriAffiliation unknown3
Frilianda WulandariAffiliation unknown2
Rudiyanto RudiyantoAffiliation unknown2
Lilis Harianti HasibuanAffiliation unknown2
Cici SaputriAffiliation unknown2
Lenny Puspita DewiUniversitas Muhammadiyah Madiun1
Yan Aditya PradanaUniversitas Muhammadiyah Madiun1
Dian Ardifah IswariUniversitas Muhammadiyah Madiun1
Naufal IshartonoMuhammadiyah University of Surakarta1
Citra Kusuma PratiwiUniversitas Muhammadiyah Madiun1
SRI WAHYUNIAffiliation unknown1
Fery MurtiningrumAffiliation unknown1
Melinda NoerAffiliation unknown1
Sisca WulandariAffiliation unknown1
Izzati Rahmi HGAffiliation unknown1
Aulia ZuardinAffiliation unknown1
Ridho SaputraAffiliation unknown1
Alvi Khairin NisaAffiliation unknown1
Nia RamadhaniAffiliation unknown1
Mawanda AlmuhayarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Covid-19 Patient Mortality Risk Classification Using Bayesian Binary Logistic Regression</dc:title>
	<dc:title xml:lang="id">Klasifikasi Risiko Kematian Pasien Covid-19 Menggunakan Regresi Logistik Biner Bayesian (RLBB)</dc:title>
	<dc:creator xml:lang="en">Shobri, Muhammad Qolbi</dc:creator>
	<dc:creator xml:lang="en">Yanuar, Ferra </dc:creator>
	<dc:creator xml:lang="en">Devianto, Dodi </dc:creator>
	<dc:subject xml:lang="en">Logistic Regression</dc:subject>
	<dc:subject xml:lang="en">Bayesian</dc:subject>
	<dc:subject xml:lang="en">Covid-19</dc:subject>
	<dc:subject xml:lang="id">Regresi Logistik</dc:subject>
	<dc:subject xml:lang="id">Metode Bayes</dc:subject>
	<dc:subject xml:lang="id">Covid-19</dc:subject>
	<dc:description xml:lang="en">At the end of 2019 the world was shocked by a new disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease is called Covid-19 (Coronavirus Disease). The mortality rate due to disease is increasing every day. In Indonesia as of April 2021, confirmed Covid-19 patients who died reached 42,530 patients, seeing the high mortality rate of Covid-19 patients so it needs to be studied further so that the risk of death of these Covid-19 patients can be minimized. This research utilizing  binary logistic regression with Bayesian method parameter estimation. In this study, the predictor variables used were in the form of categories that each category in the predictor variables was assumed to have the same risk of death risk of Covid-19 patients. The results of this study indicate that the number of comorbids has a significant effect on the risk of death of Covid-19 patients, the more the number of comorbids suffered by the patient, the higher the risk of death of the patient. The accuracy of this method in classifying data is 84.68%.</dc:description>
	<dc:description xml:lang="id">Pada akhir tahun 2019 dunia digemparkan dengan penyakit baru yang disebabkan oleh SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). Penyakit yang disebabkan oleh SARS-CoV-2 ini disebut dengan Covid-19&amp;nbsp; (Coronavirus Disease). Angka kematian akibat penyakit ini kian hari kian bertambah. Di Indonesia sendiri per April 2021 pasien yang terkonfirmasi Covid-19 yang meninggal dunia mencapai 42.530 pasien. Melihat tingginya angka kematian pasien Covid-19 sehingga perlu dikaji lebih lanjut agar upaya pencegahan risiko kematian pasien Covid-19 ini dapat diminimalisir. Penelitian ini menggunakan Regresi Logistik Biner dengan pendugaan parameter metode Bayes&amp;nbsp; dan mengukur ketepatan klasifikasi model tersebut pada data penelitian. &amp;nbsp;Pada penelitian ini variabel prediktor yang digunakan berbentuk kategori sehingga setiap kategori&amp;nbsp; pada variabel prediktor diasumsikan memiliki risiko yang sama terhadap risiko kematian pasien Covid-19. Hasil dari penelitian ini diperoleh bahwa Jumlah Komorbid signifikan mempengaruhi risiko kematian pasien Covid-19, semakin jumlah komorbid yang diderita oleh pasien maka semakin tinggi pula risiko kematian pasien tersebut. Adapun ketepatan metode ini dalam mengklasifikasi data sebesar 84,68 %.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2021-09-02</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://journal.unhas.ac.id/index.php/jmsk/article/view/14268</dc:identifier>
	<dc:identifier>10.20956/j.v18i1.14268</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 18 No. 1 (2021): September 2021; 150-160</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/14268/7314</dc:relation>
	<dc:relation>10.20956/j.v18i1.14268.g7314</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>