An improved early detection method of type-2 diabetes mellitus using multiple classifier system

Zhu, Jia, Xie, Qing and Zheng, Kai (2015) An improved early detection method of type-2 diabetes mellitus using multiple classifier system. Information Sciences, 292 1-14. doi:10.1016/j.ins.2014.08.056

Author Zhu, Jia
Xie, Qing
Zheng, Kai
Title An improved early detection method of type-2 diabetes mellitus using multiple classifier system
Journal name Information Sciences   Check publisher's open access policy
ISSN 0020-0255
Publication date 2015-01-20
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.ins.2014.08.056
Open Access Status
Volume 292
Start page 1
End page 14
Total pages 14
Place of publication Philadelphia PA, United States
Publisher Elsevier
Collection year 2015
Language eng
Abstract The specific causes of complex diseases such as Type-2 Diabetes Mellitus (T2DM) have not yet been identified. Nevertheless, many medical science researchers believe that complex diseases are caused by a combination of genetic, environmental, and lifestyle factors. Detection of such diseases becomes an issue because it is not free from false presumptions and is accompanied by unpredictable effects. Given the greatly increased amount of data gathered in medical databases, data mining has been used widely in recent years to detect and improve the diagnosis of complex diseases. However, past research showed that no single classifier can be considered optimal for all problems. Therefore, in this paper, we focus on employing multiple classifier systems to improve the accuracy of detection for complex diseases, such as T2DM. We proposed a dynamic weighted voting scheme called multiple factors weighted combination for classifiers’ decision combination. This method considers not only the local and global accuracy but also the diversity among classifiers and localized generalization error of each classifier. We evaluated our method on two real T2DM data sets and other medical data sets. The favorable results indicated that our proposed method significantly outperforms individual classifiers and other fusion methods.
Keyword Multiple classifier system
Pattern recognition
Majority vote
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 7 Sep 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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