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Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease

Recursive Journal of Informatics · Vol. 2 No. 2 (2024) · hlm. 62–70

DOI
10.15294/192vyt90
ISSN
2986-6588, 2963-5551
Terbit
2024-09-30
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates. Purpose: Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease. Methods/Study design/approach: The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML. Result/Findings: The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%. Novelty/Originality/Value: This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Arif WidiyatmokoState University of Semarang3
Muhamad TaufiqState University of Semarang2
Melissa Salma DarmawanState University of Semarang2
Indah Urwatin WusqoState University of Semarang2
Dela Rista DamayantiState University of Semarang2
Renita Arianti HapsariState University of Semarang2
Fathan ArsyadaniState University of Semarang2
Wahyu HardyantoState University of Semarang2
Epafraditus MemorianoState University of Semarang1
Agung LaksonoState University of Semarang1
Alfat MutoriqState University of Semarang1
Alfandy Himawan Bagus RafliState University of Semarang1
Syuja Zhafran Rakha KrishandhieState University of Semarang1
Kholiq BudimanState University of Semarang1
Fitri Arum SasiState University of Semarang1
Nila Prasetya AryaniAfiliasi tidak diketahui1
Defin AndestianAfiliasi tidak diketahui1
SugiyantoAfiliasi tidak diketahui1
Wahyu SetyaningrumAfiliasi tidak diketahui1
M Fadil MardiansyahAfiliasi tidak diketahui1
Muhamad Anbiya Nur IslamAfiliasi 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) 
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	<dc:title xml:lang="en">Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease</dc:title>
	<dc:creator xml:lang="en">Cahyani, Rima Ayu</dc:creator>
	<dc:creator xml:lang="en">Purwinarko, Aji</dc:creator>
	<dc:subject xml:lang="en">classification</dc:subject>
	<dc:subject xml:lang="en">prediction</dc:subject>
	<dc:subject xml:lang="en">cardiovascular</dc:subject>
	<dc:subject xml:lang="en">neural network</dc:subject>
	<dc:subject xml:lang="en">multilayer perceptron</dc:subject>
	<dc:subject xml:lang="en">Levenberg Marquardt</dc:subject>
	<dc:subject xml:lang="en">adaptive mutation PSO</dc:subject>
	<dc:description xml:lang="en">Abstract.&amp;nbsp;Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates.
Purpose:&amp;nbsp;Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease.
Methods/Study design/approach:&amp;nbsp;The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML.
Result/Findings:&amp;nbsp;The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%.
Novelty/Originality/Value:&amp;nbsp;This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.</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:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/34865</dc:identifier>
	<dc:identifier>10.15294/192vyt90</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 2 No. 2 (2024): September 2024; 62-70</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/w8ab6m87</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/34865/6325</dc:relation>
</oai_dc:dc>