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Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients

Recursive Journal of Informatics · Vol. 2 No. 2 (2024) · hlm. 71–79

DOI
10.15294/vavw2205
ISSN
2986-6588, 2963-5551
Terbit
2024-09-30
Penerbit
Universitas Negeri Semarang

Abstrak

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.

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Rekan penulisOrganisasiArtikel bersama
Riza ArifudinState University of Semarang5
Alamsyah AlamsyahState University of Semarang3
Kholiq BudimanAfiliasi tidak diketahui2
Nuril AsyrofiyyahState University of Semarang2
Dany PradanaState University of Semarang2
Dwijanto DwijantoState University of Semarang2
Amin SuyitnoState University of Semarang2
Amin SuyitnoState University of Semarang2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Anggyi Trisnawan PutraAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Yahya Nur IfrizaState University of Semarang1
Tiffany Ovilia Dwi LestariState University of Semarang1
Chairun NisakState University of Semarang1
Kevin Tito HutahaeanState University of Semarang1
Defa RiandinaState University of Semarang1
Isa AkhlisState University of Semarang1
Taufik Fajar MubaroqAfiliasi tidak diketahui1
Rahmalia NuridaAfiliasi tidak diketahui1
Apriani SolikhatunAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anita Ayu SeptiantinaAfiliasi tidak diketahui1
Arief Broto SusiloState University of Semarang1
Tomi Bagus NugrohoState University of Semarang1
Wahyu SyafrizalState University of Semarang1
Oktaria Gina KhoirunnisaState University of Semarang1
Yusuf AffandiState University of Semarang1
Erika Noor DiantiState University of Semarang1
Akbar Lintang AjiState University of Semarang1
Fabian Dominggus Eka BoroState University of Semarang1
Muhammad AfifudinState University of Semarang1
Ahmad Ramadhan AuniState University of Semarang1
Muhammad KharisAfiliasi tidak diketahui1
Fajar Eska PradhanaAfiliasi tidak diketahui1
Ratnaningtyas Widyani PurnamasariAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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">Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients</dc:title>
	<dc:creator xml:lang="en">Zaaidatunni'mah, Untsa</dc:creator>
	<dc:creator xml:lang="en">Sugiharti, Endang</dc:creator>
	<dc:subject xml:lang="en">Data Mining</dc:subject>
	<dc:subject xml:lang="en">Heart Failure</dc:subject>
	<dc:subject xml:lang="en">Random Forest</dc:subject>
	<dc:subject xml:lang="en">SMOTE</dc:subject>
	<dc:subject xml:lang="en">PSO</dc:subject>
	<dc:description xml:lang="en">Abstract.&amp;nbsp;Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique.
Purpose:&amp;nbsp;This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results.
Methods/Study design/approach:&amp;nbsp;To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases.
Result/Findings:&amp;nbsp;The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research.
Novelty/Originality/Value:&amp;nbsp;Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/34867</dc:identifier>
	<dc:identifier>10.15294/vavw2205</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 2 No. 2 (2024): September 2024; 71 - 79</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/w8ab6m87</dc:source>
</oai_dc:dc>