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Interpretable Rule-Based Clinical Decision Support for Early Screening Of Heart Disease using C4.5 Decision Trees

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 1 (2026) · pp. 59–70

DOI
10.22146/ijccs.112701
ISSN
2460-7258, 1978-1520
Published
2026-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study develops an interpretable rule-based clinical decision support system for early screening of heart disease presence by integrating the C4.5 decision tree algorithm with a rule-based reasoning mechanism. The proposed approach is intended to assist clinicians in obtaining rapid, transparent preliminary indications from clinical data, particularly in settings that require lightweight and auditable solutions. The dataset was obtained from the UCI Heart Disease Repository and comprises 299 patient records, evaluated using a 70% training and 30% testing split in RapidMiner. Experimental results show that the C4.5 model achieves an accuracy of 86.52% and produces clinically interpretable IF–THEN rules, enabling traceable reasoning and decision auditing. Although C4.5 is a classical learning algorithm, it remains relevant for clinical decision support due to its auditability, low computational cost, and ease of deployment in resource-constrained environments. The developed system is expected to support early screening/triage and data-driven clinical decision-making, contributing to the advancement of medical decision support systems (MDSS).

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links · 3 across different organisations

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  • State University of Semarang
  • Universitas Teknologi Digital Indonesia
  • Taipei Medical University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ainurohmah AinurrohmahState University of Semarang1

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">Interpretable Rule-Based Clinical Decision Support for Early Screening Of Heart Disease using C4.5 Decision Trees</dc:title>
	<dc:creator>Andriyani, Widyastuti</dc:creator>
	<dc:creator>Wiyanti, Dian Tri</dc:creator>
	<dc:creator>Nugroho, Daniel C.A.</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">C4.5 algorithm, rule-based reasoning, expert system, heart disease, medical decision support</dc:subject>
	<dc:description xml:lang="en-US">This study develops an interpretable rule-based clinical decision support system for early screening of heart disease presence by integrating the C4.5 decision tree algorithm with a rule-based reasoning mechanism. The proposed approach is intended to assist clinicians in obtaining rapid, transparent preliminary indications from clinical data, particularly in settings that require lightweight and auditable solutions. The dataset was obtained from the UCI Heart Disease Repository and comprises 299 patient records, evaluated using a 70% training and 30% testing split in RapidMiner. Experimental results show that the C4.5 model achieves an accuracy of 86.52% and produces clinically interpretable IF–THEN rules, enabling traceable reasoning and decision auditing. Although C4.5 is a classical learning algorithm, it remains relevant for clinical decision support due to its auditability, low computational cost, and ease of deployment in resource-constrained environments. The developed system is expected to support early screening/triage and data-driven clinical decision-making, contributing to the advancement of medical decision support systems (MDSS).</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-01-31</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/112701</dc:identifier>
	<dc:identifier>10.22146/ijccs.112701</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 1 (2026): January; 59-70</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/112701/43430</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 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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