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Evaluation of Fuzzy Relation Method for Medical Decision Support

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Abstract

The potential of computer based tools to assist physicians in medical decision making, was envisaged five decades ago. Apart from factors like usability, integration with work-flow and natural language processing, lack of decision accuracy of the tools has hindered their utility. Hence, research to develop accurate algorithms for medical decision support tools, is required. Pioneering research in last two decades, has demonstrated the utility of fuzzy set theory for medical domain. Recently, Wagholikar and Deshpande proposed a fuzzy relation based method (FR) for medical diagnosis. In their case studies for heart and infectious diseases, the FR method was found to be better than naive bayes (NB). However, the datasets in their studies were small and included only categorical symptoms. Hence, more evaluative studies are required for drawing general conclusions. In the present paper, we compare the classification performance of FR with NB, for a variety of medical datasets. Our results indicate that the FR method is useful for classification problems in the medical domain, and that FR is marginally better than NB. However, the performance of FR is significantly better for datasets having high proportion of unknown attribute values. Such datasets occur in problems involving linguistic information, where FR can be particularly useful. Our empirical study will benefit medical researchers in the choice of algorithms for decision support tools.

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Acknowledgements

We thank Dr. Bryan Todd, for contributing the gynaecology dataset, and UCI administrators for maintaining the UCI datasets. We are grateful to Dr. S.R. Gadre for making computational facilities available for this work and to the reviewers for their excellent suggestions.

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Authors and Affiliations

  1. Interdisciplinary School of Scientific Computing, University of Pune, Pune, 411007, India

    Kavishwar Wagholikar

  2. Sanjeevan Hospital, Erandwana Gaothan, Karve Road, Pune, 411004, India

    Sanjeev Mangrulkar

  3. BISC-SIG-EMS, Department of EECS, University of California, Berkeley, CA, 94720, USA

    Ashok Deshpande

  4. SECG, Centre for Development of Advanced Computing, Pune, 411007, India

    Vijayraghavan Sundararajan

Authors
  1. Kavishwar Wagholikar
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  2. Sanjeev Mangrulkar
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  3. Ashok Deshpande
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  4. Vijayraghavan Sundararajan
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Correspondence to Kavishwar Wagholikar.

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Wagholikar, K., Mangrulkar, S., Deshpande, A. et al. Evaluation of Fuzzy Relation Method for Medical Decision Support. J Med Syst 36, 233–239 (2012). https://doi.org/10.1007/s10916-010-9472-5

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  • DOI: https://doi.org/10.1007/s10916-010-9472-5

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