A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis

被引:61
作者
Chen, Hui-Ling [1 ,2 ]
Yang, Bo [1 ,2 ]
Wang, Gang [1 ,2 ]
Liu, Jie [1 ,2 ]
Chen, Yi-Dong [3 ]
Liu, Da-You [1 ,2 ]
机构
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
[2] Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China
[3] Chinese Peoples Armed Police Force, Hangzhou Command Coll, Hangzhou 310023, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Thyroid disease diagnosis; Support; Vector machines; Expert system; Fisher score; Particle swarm optimization;
D O I
10.1007/s10916-011-9655-8
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
引用
收藏
页码:1953 / 1963
页数:11
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