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Open access Jurnal SINTA 2

Support Vector Machine for Accurate Classification of Diabetes Risk Levels

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 3 (2025) · pp. 317–326

DOI
10.22146/ijccs.107740
ISSN
2460-7258, 1978-1520
Published
2025-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This research explores the application of Support Vector Machines (SVM) for accurately classifying diabetes risk levels based on a publicly available dataset containing 768 instances and 9 attributes, including glucose levels, BMI, blood pressure, and insulin levels. The model's systematic development process involved data preprocessing, feature selection, and hyperparameter optimization to ensure robust performance. Results indicate an overall accuracy of 76%, with high precision and recall for the non-diabetic risk class, but relatively lower performance for the diabetic risk class, highlighting the challenges posed by class imbalance and overlapping data features. To address these issues, future research should incorporate advanced resampling techniques, refined feature engineering, and alternative machine learning models like Random Forest or XGBoost. This research underscores the potential of SVM as a valuable tool for early diabetes detection, offering healthcare professionals a reliable means to identify at-risk individuals and personalize intervention strategies. By bridging theoretical advancements and practical applications, the research contributes to enhancing predictive analytics in medical diagnostics, paving the way for improved patient outcomes and efficient public health management

Keywords

Collaboration network of the authors

9 authors · 16 co-authorship links · 9 across different organisations

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

  • Institut Bisnis dan Teknologi Indonesia
  • Telkom University
  • Okayama University
  • Politeknik Negeri Malang
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
I Gede AdnyanaInstitut Bisnis dan Teknologi Indonesia1
I Nyoman Buda HartawanInstitut Bisnis dan Teknologi Indonesia1
Bayu Rima AdityaTelkom University1
Aldilla IradiantyTelkom 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">Support Vector Machine for Accurate Classification of Diabetes Risk Levels</dc:title>
	<dc:creator>Sugiartawan, Putu</dc:creator>
	<dc:creator>Wardani, Ni Wayan</dc:creator>
	<dc:creator>Pradhana, Anak Agung Surya</dc:creator>
	<dc:creator>Batubulan, Kadek Suarjuna</dc:creator>
	<dc:creator>Kotama, I Nyoman Darma</dc:creator>
	<dc:subject xml:lang="en-US">Informatic</dc:subject>
	<dc:subject xml:lang="en-US">Support Vector Machine; Diabetes Risk; Classification Model; Machine Learning; Predictive Analytics</dc:subject>
	<dc:description xml:lang="en-US">This research explores the application of Support Vector Machines (SVM) for accurately classifying diabetes risk levels based on a publicly available dataset containing 768 instances and 9 attributes, including glucose levels, BMI, blood pressure, and insulin levels. The model's systematic development process involved data preprocessing, feature selection, and hyperparameter optimization to ensure robust performance. Results indicate an overall accuracy of 76%, with high precision and recall for the non-diabetic risk class, but relatively lower performance for the diabetic risk class, highlighting the challenges posed by class imbalance and overlapping data features. To address these issues, future research should incorporate advanced resampling techniques, refined feature engineering, and alternative machine learning models like Random Forest or XGBoost. This research underscores the potential of SVM as a valuable tool for early diabetes detection, offering healthcare professionals a reliable means to identify at-risk individuals and personalize intervention strategies. By bridging theoretical advancements and practical applications, the research contributes to enhancing predictive analytics in medical diagnostics, paving the way for improved patient outcomes and efficient public health management</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Master of Informatics Programs</dc:contributor>
	<dc:date>2025-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/107740</dc:identifier>
	<dc:identifier>10.22146/ijccs.107740</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 3 (2025): July; 317-326</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/107740/42104</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/downloadSuppFile/107740/38932</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 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>
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