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Hybrid Data Mining Ensemble for Predicting Osteoporosis Risk

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3 Author(s)
Wenjia Wang ; Sch. of Comput. Sci., East Anglia Univ., Norwich ; Richards, G. ; Rea, S.

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

Published in:

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Date of Conference:

17-18 Jan. 2006