The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus
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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“Although AI can classify different types of diabetes more accurately,51,54,55 further algorithm upgrading is required to meet the clinical treatment needs.”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [54]
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1992) · Cited by 23,431
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
International Diabetes Federation 2017
Carracher, A. M., Marathe, P. H., & Close, K. L. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12644 · Cited by 13,379
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053
Witten, I. H. (2008) · Cited by 9,143
Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987
Alberti, K. G. M. M., Zimmet, P., & Shaw, J. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.01858.x · Cited by 6,597
Research works printed but not linked: 5
- lavrac, 2000, Encyclopedia of Computer Science and Technology vol 42, 113
- huang, 2004, Lecture Notes Artificial Intelligence vol 3275, 153
- 2007, international diabetes federation, Diabetes Atlas
- 2007, Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia
- japkowicz, 0, the class imbalance problem: significance and strategies, Proc Int Conf Artif Intell ( IC - AI 2000), 111
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The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2023.100333 · Cited by 568
Feature selection and classification systems for chronic disease prediction: A review
Jain, D. K., & Singh, V. P. (2018) · Egyptian Informatics Journal · doi:10.1016/j.eij.2018.03.002 · Cited by 369
Deep learning approach for diabetes prediction using PIMA Indian dataset
Naz, H. U., & Ahuja, S. (2020) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-020-00520-5 · Cited by 359
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325
Metadata
- 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
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