Cited by 367Artificial Intelligence in HealthcareExplainable Artificial Intelligence (XAI)Data Mining and Machine Learning Applications

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Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

Barakat, Nahla, Bradley, Andrew P, Barakat, Mohamed Nabil H·2010·IEEE Transactions on Information Technology in Biomedicine

DOI 10.1109/titb.2009.2039485

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Abstract

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. According to the International Diabetes Federation, there are currently 246 million diabetic people worldwide, and this number is expected to rise to 380 million by 2025. Furthermore, 3.8 million deaths are attributable to diabetes complications each year. It has been shown that 80% of type 2 diabetes complications can be prevented or delayed by early identification of people at risk. In this context, several data mining and machine learning methods have been used for the diagnosis, prognosis, and management of diabetes. In this paper, we propose utilizing support vector machines (SVMs) for the diagnosis of diabetes. In particular, we use an additional explanation module, which turns the "black box" model of an SVM into an intelligible representation of the SVM's diagnostic (classification) decision. Results on a real-life diabetes dataset show that intelligible SVMs provide a promising tool for the prediction of diabetes, where a comprehensible ruleset have been generated, with prediction accuracy of 94%, sensitivity of 93%, and specificity of 94%. Furthermore, the extracted rules are medically sound and agree with the outcome of relevant medical studies.

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Authors: Barakat, N., Bradley, A. P., & Barakat, M. N. H.
Affiliations: German University of Technology (OM)
The University of Queensland (AU)
Ministry of Health (OM)
ORCID: Barakat, Nahla
Bradley, Andrew P
Venue: IEEE Transactions on Information Technology in Biomedicine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2010-07
Volume: 14
Issue: 4
Pages: 1114-1120
ISSN: 1089-7771 (ISSN-L)
DOI: 10.1109/titb.2009.2039485
OpenAlex: W2118414527
Cited by: 367
References: 40
Citations by year: 2026: 14 · 2025: 16 · 2024: 28 · 2023: 28 · 2022: 40 · 2021: 34 · 2020: 40 · 2019: 40 · 2018: 31 · 2017: 16 · 2016: 19 · 2015: 13 · 2014: 13 · 2013: 13 · 2012: 7
FWCI: 20.87
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: diabetes diagnosis, support vector machine, explainable machine learning, interpretable classification, rule extraction, type 2 diabetes, early risk detection, clinical decision support
MeSH terms: Diabetes Mellitus, Humans, Information Storage and Retrieval

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Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

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