NDLI: Hybrid Data Mining Ensemble for Predicting Osteoporosis Risk

Content Provider	IEEE Xplore Digital Library
Author	Wenjia Wang Richards, G. Rea, S.
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci., East Anglia Univ., Norwich (Wenjia Wang; Richards, G.)
Abstract	This paper presents the research in developing data mining ensembles for predicting the risk of osteoporosis prevalence in women. Osteoporosis is a bone disease that commonly occurs among postmenopausal women and no effective treatments are available at the moment, except prevention, which requires early diagnosis. However, early detection of the disease is very difficult. This research aims to devise an intelligent diagnosis support system by using data mining ensemble technology to assist general practitioners assessing patient's risk at developing osteoporosis. The paper describes the methods for constructing effective ensembles through measuring diversity between individual predictors. Hybrid ensembles are implemented by neural networks and decision trees. The ensembles built for predicting osteoporosis are evaluated by the real-world data and the results indicate that the hybrid ensembles have relatively high-level of diversity and thus are able to improve prediction accuracy
Starting Page	886
Ending Page	889
File Size	155624
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1616557
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Osteoporosis Bone diseases Competitive intelligence Neural networks Testing Hospitals Hybrid intelligent systems Accuracy Skeleton
Content Type	Text
Resource Type	Article

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