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

Handle URI:
http://hdl.handle.net/10754/563994
Title:
An improved early detection method of type-2 diabetes mellitus using multiple classifier system
Authors:
Zhu, Jia; Xie, Qing ( 0000-0003-4530-588X ) ; Zheng, Kai
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Elsevier BV
Journal:
Information Sciences
Issue Date:
Jan-2015
DOI:
10.1016/j.ins.2014.08.056
Type:
Article
ISSN:
00200255
Sponsors:
This work was supported by the National Natural Science Foundation of China (No. 61272067), the Natural Science Foundation of Guangdong Province, China (No. S2012030006242) and the National High Technology Research and Development Program of China (863, No. 2013AA01A212).
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhu, Jiaen
dc.contributor.authorXie, Qingen
dc.contributor.authorZheng, Kaien
dc.date.accessioned2015-08-03T12:22:18Zen
dc.date.available2015-08-03T12:22:18Zen
dc.date.issued2015-01en
dc.identifier.issn00200255en
dc.identifier.doi10.1016/j.ins.2014.08.056en
dc.identifier.urihttp://hdl.handle.net/10754/563994en
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (No. 61272067), the Natural Science Foundation of Guangdong Province, China (No. S2012030006242) and the National High Technology Research and Development Program of China (863, No. 2013AA01A212).en
dc.publisherElsevier BVen
dc.subjectClassificationen
dc.subjectMultiple classifier systemen
dc.subjectT2DMen
dc.titleAn improved early detection method of type-2 diabetes mellitus using multiple classifier systemen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalInformation Sciencesen
dc.contributor.institutionSchool of Computer Science, South China Normal University, Chinaen
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, University of Queensland, Australiaen
kaust.authorXie, Qingen
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