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

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Abstract

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.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60873149, 60973088, 60773099 and the National High-Tech Research and Development Plan of China under Grant Nos. 2006AA10Z245, 2006AA10A309. This work is also supported by the Open Projects of Shanghai Key Laboratory of Intelligent Information Processing in Fudan University under the Grand No. IIPL-09-007, the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) and the basic scientific research fund of Chinese Ministry of Education.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, No.2699 Qianjin Road, Changchun, Jilin, 130012, China

    Hui-Ling Chen, Bo Yang, Gang Wang, Jie Liu & Da-You Liu

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, Jilin, 130012, China

    Hui-Ling Chen, Bo Yang, Gang Wang, Jie Liu & Da-You Liu

  3. Hangzhou Command College of Chinese People’s Armed Police Force, No.377 Tianmushan Road, Hangzhou, Zhejiang, 310023, China

    Yi-Dong Chen

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  1. Hui-Ling Chen
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  2. Bo Yang
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Correspondence to Da-You Liu.

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Chen, HL., Yang, B., Wang, G. et al. A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis. J Med Syst 36, 1953–1963 (2012). https://doi.org/10.1007/s10916-011-9655-8

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