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

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

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.

Integrity signals

Registry

Crossref · 10.1109/titb.2009.2039485

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI can classify different types of diabetes more accurately, but further algorithm upgrading is required to meet clinical treatment needs.

    “Although AI can classify different types of diabetes more accurately,51,54,55 further algorithm upgrading is required to meet the clinical treatment needs.”

Cites

Flagged first, then by citations

1 of the 40 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

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)
Venue
IEEE Transactions on Information Technology in Biomedicine
Published
2010-07
Volume
14
Issue
4
Pages
1114-1120
ISSN
1089-7771 (ISSN-L)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review