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Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization

Recursive Journal of Informatics · Vol. 3 No. 1 (2025) · hlm. 26–33

DOI
10.15294/rji.v3i1.737
ISSN
2986-6588, 2963-5551
Terbit
2025-03-28
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements. Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection. Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.

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24 penulis · 33 tautan kepenulisan bersama · 4 lintas organisasi

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  • State University of Semarang
  • National University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Deonisius Germandy Cahaya PutraState University of Semarang2
Vivi WulandariState University of Semarang2
Resta AdityatamaState University of Semarang2
Muh. SholehState University of Semarang1
Martanto Setyo HusodoState University of Semarang1
Taripar Matius AlexanderState University of Semarang1
Florentina Yuni AriniState University of Semarang1
Riza ArifudinState University of Semarang1
Athoillah AthoillahState University of Semarang1
Cahya WulandariState University of Semarang1
Muhamad Sayuti HassanNational University of Malaysia1
Zidney Ilma Fazaada EmhaState University of Semarang1
Sugianto SugiantoState University of Semarang1
Angel Novita SinuratState University of Semarang1
Tazkiyatun Aeni AmaliyahState University of Semarang1
Ahmad WarsunState University of Semarang1
Pradistia Edo KristiantoState University of Semarang1
Syehudin SyehudinState University of Semarang1
Kholiq BudimanState University of Semarang1
Annisya Dira PrastiwiState University of Semarang1
Adham Satria FirmansyahState University of Semarang1
Muhammad Zaenal MuttaqinState University of Semarang1

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://journal.unnes.ac.id/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization</dc:title>
	<dc:creator xml:lang="en">Pratama, Luthfi Ilham Agus</dc:creator>
	<dc:creator xml:lang="en">Putra, Anggyi Trisnawan</dc:creator>
	<dc:subject xml:lang="en">Heart Disease</dc:subject>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">SVM</dc:subject>
	<dc:subject xml:lang="en">Hybrid Particle Swarm and Grey Wolf Optimization</dc:subject>
	<dc:subject xml:lang="en">RJI</dc:subject>
	<dc:description xml:lang="en">Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements.             Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection.              Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-03-28</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:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/737</dc:identifier>
	<dc:identifier>10.15294/rji.v3i1.737</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 3 No. 1 (2025): March 2025; 26-33</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v3i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/737/3432</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Recursive Journal of Informatics</dc:rights>
</oai_dc:dc>