Classification of Diabetes Disease using TCM Electronic Nose Signals and Ensemble Learning

被引：0

作者：

Li, Qiang ^{[1
]}

Liu, Li-Sang ^{[2
]}

Yang, Fan ^{[1
]}

Zheng, Zhe-Zhou ^{[3
]}

Lin, Xue-Juan ^{[3
]}

Wu, Qing-Hai ^{[3
]}

机构：

[1] Xiamen Univ, Sch Informat Sci & Engn, Xiamen, Peoples R China

[2] Fujian Univ Technol, Sch Informat Sci & Engn, Fuzhou, Peoples R China

[3] Fujian Univ Tradit Chinese Med, Sch Tradit Chinese Med, Fuzhou, Peoples R China

来源：

2014 PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2014) | 2014年

关键词：

TCM electronic nose; SVM ensemble; diabetes disease diagnosis; LUNG-CANCER; DIAGNOSIS; BREATH;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：507 / 511

页数：5

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