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Open access Jurnal SINTA 2

Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 2 (2024)

DOI
10.22146/ijccs.94575
ISSN
2460-7258, 1978-1520
Published
2024-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively.

Keywords

Collaboration network of the authors

31 authors · 72 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 Ahmad Dahlan
  • Ajou University
  • Universiti Teknikal Malaysia Melaka
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anton YudhanaUniversitas Ahmad Dahlan4
HermanUniversitas Ahmad Dahlan2
Herman YuliansyahUniversitas Ahmad Dahlan2
Rusydi UmarUniversitas Ahmad Dahlan2
Sunardi SunardiUniversitas Ahmad Dahlan2
Muhajir YunusUniversitas Ahmad Dahlan2
Dianda RifaldiUniversitas Ahmad Dahlan1
Bella Okta Sari MirandaUniversitas Ahmad Dahlan1
Dewi SoyusiawatyUniversitas Ahmad Dahlan1
Farhan Radhiansyah RazakUniversitas Ahmad Dahlan1
Yuminah yuminahUniversitas Ahmad Dahlan1
Suprianto SupriantoAffiliation unknown1
Frandika SeptaUniversitas Ahmad Dahlan1
Imam RiadiUniversitas Ahmad Dahlan1
Siti HelmiyahUniversitas Ahmad Dahlan1
Abdullah HanifUniversitas Ahmad Dahlan1
Musri Iskandar NasutionUniversitas Ahmad Dahlan1
Kharis Hudaiby HanifUniversitas Ahmad Dahlan1
Herman HermanAffiliation unknown1
Aldibangun Pidekso PutroAffiliation unknown1
Herman HermanAffiliation unknown1
Wahid Alfaridsi Achmad ZeinAffiliation unknown1
Eko PriantoUniversitas Ahmad Dahlan1
Tole SutiknoUniversitas Ahmad Dahlan1
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka1
Hendril Satrian PurnamaAffiliation unknown1
Anggit PamungkasAffiliation unknown1
Ibrahim Mohd AlsofyaniAjou University1

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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm</dc:title>
	<dc:creator>Jogo Samodro, Maulana Muhamammad</dc:creator>
	<dc:creator>Biddinika, Muhammad Kunta</dc:creator>
	<dc:creator>Fadlil, Abdul</dc:creator>
	<dc:subject xml:lang="en-US">Infromatics</dc:subject>
	<dc:subject xml:lang="en-US">Diabetes, Multilayer Perceptron, Machine Learning</dc:subject>
	<dc:description xml:lang="en-US">In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Ahmad Dahlan</dc:contributor>
	<dc:date>2024-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/94575</dc:identifier>
	<dc:identifier>10.22146/ijccs.94575</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 2 (2024): April</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/94575/39108</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>