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Developing Transformed Fuzzy Neural Networks to Enhance Medical Data Classification Accuracy

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Abstract

Developing an automatic model to precisely and quickly classify medical data is a challenging work due to lack of medical knowledge. This paper presented a transformed fuzzy neural network (TFNN) to enhance medical data classification accuracy by learning knowledge from the available medical databases. We developed a novel reward/penalty fuzzy rule base to infer the degree of transformation for each input attribute. The degree of reward/penalty was determined according to its original measured value relative to its attribute mean and standard deviation. The farther the deviation of a datum to its mean value, the larger the penalty was exerted on the datum. In contrast, a datum in the neighborhood of mean value received a positive award. Furthermore, a pattern’s desired output was considered a valuable knowledge and was treated as an extra input to the TFNN. In this study, three medical datasets are used to validate the classification accuracy. Results compared to the back propagation neural network and support vector machine verified the effectiveness of the proposed model in medical data classification.

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Acknowledgements

This study was funded in part by the Ministry of Science and Technology, Taiwan, under Grants MOST105-2221-E027-042 and MOST106-2221-E-027-001, in part by the joint project between the National Taipei University of Technology and Mackay Memorial Hospital under Grants NTUT-MMH-105-04, NTUT-MMH-106-03, MMH-TT-10602, MMH-TT-10403, and MMH-TT-10504.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Yo-Ping Huang & Avichandra Singh

  2. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, 23741, Taiwan

    Yo-Ping Huang

  3. Department of Psychiatry, Mackay Memorial Hospital, Taipei, 10449, Taiwan

    Shen-Ing Liu

  4. Department of Psychiatry and Center for Suicide Prevention, Mackay Memorial Hospital, Taipei, 10449, Taiwan

    Shu-I Wu

  5. Department of Thermal Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam

    Hoang An Quoc

  6. Antal Bejczy Center of Intelligent Robotics, Óbuda University, Budapest, Hungary

    Andrea Sereter

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  1. Yo-Ping Huang
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  2. Avichandra Singh
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  3. Shen-Ing Liu
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  4. Shu-I Wu
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Correspondence to Yo-Ping Huang.

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Huang, YP., Singh, A., Liu, SI. et al. Developing Transformed Fuzzy Neural Networks to Enhance Medical Data Classification Accuracy. Int. J. Fuzzy Syst. 20, 1925–1937 (2018). https://doi.org/10.1007/s40815-018-0503-6

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  • DOI: https://doi.org/10.1007/s40815-018-0503-6

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