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Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease

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Abstract

In this paper, we present an enhanced fuzzy k-nearest neighbor (FKNN) classifier based computer aided diagnostic (CAD) system for thyroid disease. The neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the particle swarm optimization (PSO) approach. The adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. In addition, we have validated the effectiveness of the principle component analysis (PCA) in constructing a more discriminative subspace for classification. The effectiveness of the resultant CAD system, termed as PCA-PSO-FKNN, has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation (CV) analysis, with the mean accuracy of 98.82% and with the maximum accuracy of 99.09%. Promisingly, the proposed CAD system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

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Notes

  1. http://www.clevelandclinic.org/health/health-info/docs/2000/2011.asp, last accessed November 23, 2011.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60873149, 60973088, 60773099. This work is also supported by the Open Projects of Shanghai Key Laboratory of Intelligent Information Processing in Fudan University under the Grant No. IIPL-09-007.

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Authors and Affiliations

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

    Da-You Liu, Hui-Ling Chen, Bo Yang, Li-Na Li & Jie Liu

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

    Da-You Liu, Hui-Ling Chen, Bo Yang, Li-Na Li & Jie Liu

  3. School of Psychology, Beijing Normal University, No.19 Xinjiekouwai Street, Haidian District, Beijing, 100875, China

    Xin-En Lv

  4. OuJiang College, Wen Zhou University, Wen Zhou, 325000, China

    Xin-En Lv

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  1. Da-You Liu
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  2. Hui-Ling Chen
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  5. Li-Na Li
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Correspondence to Jie Liu.

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Liu, DY., Chen, HL., Yang, B. et al. Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease. J Med Syst 36, 3243–3254 (2012). https://doi.org/10.1007/s10916-011-9815-x

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