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	Classification of diabetes disease using TCM electronic nose signals and ensemble learning
	Li, Qiang ; Yang, Fan ; Liu, Li-Sang ; Zheng, Zhe-Zhou ; Lin, Xue-Juan ; Wu, Qing-Hai ; Yang F(杨帆)
	2014
英文摘要	Conference Name:9th International Conference on Computer Science and Education, ICCCSE 2014. Conference Address: Vancouver, BC, Canada. Time:August 22, 2014 - August 24, 2014.; Diabetes is one of the most prevalent diseases in medical field. We propose an ensemble method for diagnosis of diabetes on traditional Chinese medicine electronic nose signals. To evaluate the effectiveness of our method, we carry out the experiments by comparing single classifier with ensemble classifiers based on support vector machine and logistic classification model. The proposed method shows better classification performance with accuracy of 88.04%. The results of this study show that ensemble method is effective to detect diabetes.
语种	英语
出处	http://dx.doi.org/10.1109/ICCSE.2014.6926513
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86947]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Li, Qiang,Yang, Fan,Liu, Li-Sang,et al. Classification of diabetes disease using TCM electronic nose signals and ensemble learning. 2014-01-01.

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