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Prediction of diabetes mellitus based on boosting ensemble modeling


Ali, R and Siddiqi, MH and Idris, M and Kang, BH and Lee, S, Prediction of diabetes mellitus based on boosting ensemble modeling, Lecture Notes in Computer Science 8867: Proceedings of the 8th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2014), 2-5 December 2014, Belfast, UK, pp. 25-28. ISSN 0302-9743 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer International Publishing Switzerland

DOI: doi:10.1007/978-3-319-13102-3_6


Healthcare systems provide personalized services in wide spread domains to help patients in fitting themselves into their normal activities of life. This study is focused on the prediction of diabetes types of patients based on their personal and clinical information using a boosting ensemble technique that internally uses random committee classifier. To evaluate the technique, a real set of data containing 100 records is used. The prediction accuracy obtained is 81.0% based on experiments performed in Weka with 10-fold cross validation.

Item Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Information systems
Research Field:Information systems not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Information services not elsewhere classified
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:98416
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2015-02-13
Last Modified:2018-03-28

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