A classifier fusion approach to osteoporosis prediction for women in Taiwan

Abstract

Osteoporosis is a progressive bone disease which will increase the risk of fracture. Related studies show that women are much more likely to get osteoporosis than men. In Taiwan, bone mineral density examination (BMD) is not included in basic health examination and needs additional payments. To reduce the healthcare burden on the government and the public, this study proposed a classifier fusion approach to develop an osteoporosis prediction model based on the past health examination data (including the basic health examination and paid BMD examination) for women in Taiwan. The proposed classifier fusion approach integrates genetic algorithm (GA), classifier and voting scheme into a GA-based ensemble classifier (EC) with the consideration of feature selection, parameter optimization and multidimensional forecasting. The results show that GA-based EC with an average classification accuracy rate of 70.43% outperforms individual classifier. Based on prediction results, the high-risk patients will be recommended to subsequently BMD examinations.

Keywords:

• osteoporosis
• prediction model
• genetic algorithm
• ensemble classifier

Acknowledgement

This paper is one of the best papers in the 14’ Chinese Institute of Industrial Engineers Conference (CIIE2014). We would particularly like to thank the special issue guest editor, Yiyo Kuo, invites us to contribute this paper for a special issue on a topic of CIIE2014.

Disclosure statement

No potential conflict of interest was reported by the authors.