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A K-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

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Abstract

This paper proposes a new medical diagnosis algorithm that uses a K-means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K-means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K-means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient approach. The Lyapunov theory is also used to verify the convergence and stability of the proposed method. The performance of the system is evaluated using several medical datasets from the University of California at Irvine (UCI). All of the experimental and comparison results are presented to demonstrate the effectiveness of the proposed medical diagnosis algorithm.

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Acknowledgements

The authors appreciate the financial support in part from the Ministry of Science and Technology of Republic of China under Grant MOST 106-2221-E-155-MY3.

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Authors and Affiliations

  1. Yuan Ze University, Taoyuan, Taiwan

    Tien-Loc Le, Tuan-Tu Huynh & Chih-Min Lin

  2. Department of Electrical Electronic and Mechanical Engineering, Lac Hong University, Bien Hoa, Vietnam

    Tien-Loc Le & Tuan-Tu Huynh

  3. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

    Lo-Yi Lin

  4. Department of Cognitive Science, Xiamen University, Xiamen, China

    Fei Chao

Authors
  1. Tien-Loc Le
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  2. Tuan-Tu Huynh
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Correspondence to Chih-Min Lin.

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Le, TL., Huynh, TT., Lin, LY. et al. A K-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis. Int. J. Fuzzy Syst. 21, 2258–2269 (2019). https://doi.org/10.1007/s40815-019-00730-x

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  • DOI: https://doi.org/10.1007/s40815-019-00730-x

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