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.
Similar content being viewed by others
References
Shortliffe, E. H., and Cimino, J. J., Biomedical informatics: Computer applications in health care and biomedicine. Springer, 2006.
Hibble, A., Kanka, D., Pencheon, D., and Pooles, F., Guidelines in general practice: The new tower of babel? Br. Med. J. 317(7162):862–863, 1998.
Jackson, P., Introduction to Expert Systems, 3rd Edn. Addison Wesley, 1998.
Miller, R. A., Medical diagnostic decision support systems—Past, present, and future: A threaded bibliography and brief commentary. J. Am. Med. Inform. Assoc. 1(1):8–27, 1994.
Ledley, R. S., and Lusted, L. B., Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 130(3366):9–21, 1959.
de Dombal, F. T., Leaper, D. J., Staniland, J. R., McCann, A. P., and Horrocks, J. C., Computer-aided diagnosis of acute abdominal pain. Br. Med. J. 2(5804):9–13, 1972.
Shortliffe, E. H., Davis, R., Axline, S. G., Buchanan, B. G., Green, C. C., and Cohen, S. N., Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the mycin system. Comput. Biomed. Res. (An International Journal), 8(4):303–320, 1975.
Burnside, E. S., Bayesian networks: Computer-assisted diagnosis support in radiology. Acad. Radiol. 12(4):422–430, 2005.
Sanchez, E., Inverses of fuzzy relations. Application to possibility distributions and medical diagnosis. Fuzzy Sets Syst. 2(1):75–86, 1979.
Adlassnig, K., The section on medical expert and knowledge-based systems at the Department of Medical Computer Sciences of the University of Vienna Medical School. Artif. Intell. Med. 21(1–3):139–146, 2001.
Ramnarayan, P., Cronje, N., Brown, R., Negusm, R., Coode, B., Moss, P., Hassan, T., Hamer, W., and Britto, J., Validation of a diagnostic reminder system in emergency medicine: A multi-centre study. Emerg. Med. J. 24(9):619–624, 2007.
Cohen, M. E., and Hudson, D. L., Meta neural networks as intelligent agents for diagnosis. In: International Joint Conference on Neural Networks, Vol. 1, pp. 233–238, 2002.
Güler, I., and Ubeyli, E. D., Multiclass support vector machines for eeg-signals classification. IEEE Trans. Inf. Technol. Biomed. 11(2):117–126, 2007.
Liu, J. L., Wyatt, J. C., Deeks, J. J., Clamp, S., Keen, J., Verde, P., Ohmann, C., Wellwood, J., Dawes, M., and Altman, D. G., Systematic reviews of clinical decision tools for acute abdominal pain. Health Technol. Assess. 10(47), 2006.
Adams, I. D., Chan, M., Clifford, P. C., Cooke, W. M., Dallos, V., de Dombal, F. T., Edwards, M. H., Hancock, D. M., Hewett, D. J., and McIntyre, N., Computer aided diagnosis of acute abdominal pain: A multicentre study. Br. Med. J. (Clin. Res. Ed.) 293(6550):800–804, 1986.
Todd, B. S., and Stamper, R., The relative accuracy of a variety of medical diagnostic programs. Methods Inf. Med. 33(4):402–416, 1994.
Ohmann, C., Moustakis, V., Yang, Q., and Lang, K., Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain. Acute abdominal pain study group. Artif. Intell. Med. 8(1):23–36, 1996.
Domingos, P., and Pazzani, M., On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29(2):103–130, 1997.
Joly, H., Sanchez, E., Gouvernet, J., and Valty, J., Applications of fuzzy set theory to the evaluation of cardiac function. In: Lindberg, D. A. B., and Kaihara, S. (Eds.), MedInfo’80, pp. 91–95. North Holland Amsterdam, 1980.
Soula, G., Gouvernet, J., Barre, A., and Marco, J. S., Application of fuzzy relations to medical decision making. In: Medinfo 80, 1980.
Esogbue, A. O., and Elder, R. C., Fuzzy sets and the modelling of physician decision processes, part I: The initial interview-information gathering session. Fuzzy Sets Syst. 2(4):279–291, 1979.
Adlassnig, K. P., Scheithauer, W., and Grabner, G., Computer-assisted diagnosis and its application in pancreatic diseases. Acta Med. Austriaca 11(3–4):125–134, 1984.
Adlassnig, K. P., Kolarz, G., Scheithauer, W., and Grabner, H., Approach to a hospital-based application of a medical expert system. Med. Inform. (Lond.) 11(3):205–223, 1986.
Sageder, B., Boegl, K., Adlassnig, K. P., Kolousek, G., and Trummer, B., The knowledge model of medframe/cadiag-iv. Stud. Health Technol. Inform. 43(Pt B):629–633, 1997.
Nagy, S., Hayde, M., Panzenböck, B., Adlassnig, K. P., and Pollak, A., Toxopert-i: Knowledge-based automatic interpretation of serological tests for toxoplasmosis. Comput. Methods Programs Biomed. 53(2):119–133, 1997.
Yen, G. G., and Meesad, P., Constructing a fuzzy rule-based system using the ilfn network and genetic algorithm. Int. J. Neural Syst. 11(5):427–443, 2001.
Tsipouras, M. G., Voglis, C., and Fotiadis, D. I., A framework for fuzzy expert system creation—Application to cardiovascular diseases. IEEE Trans. Biomed. Eng. 54(11):2089–2105, 2007.
Krajnak, M., and Xue, J., Optimizing fuzzy clinical decision support rules using genetic algorithms. In: International Conference of IEEE Engineering in Medicine and Biology Society, Vol. 1, pp. 5173–5176. GE Healthcare Information Technology: Milwaukee, WI 53226, USA, 2006.
Wagholikar, K. B., and Deshpande, A. W., Fuzzy relation based modeling for medical diagnostic decision support: Case studies. Int. J. Knowl.-Based Intel. Eng. Syst. 12(5,6):319–326, 2008.
Kohavi, R., Becker, B., and Sommerfield, D., Improving simple bayes. In: The Ninth European Conference on Machine Learning. Springer-Verlag: New York, 1997.
Adlassnig, K. P., and Kolarz, G., Representation and semiautomatic acquisition of medical knowledge in cadiag-1 and cadiag-2. Comput. Biomed. Res. 19(1):63–79, 1986.
Hand, D. J., and Till, R. J., A simple generalisation of the area under the roc curve for multiple class classification problems. Mach. Learn. 45(2):171–186, 2001.
Asuncion, A., and Newman, D. J., UCI Machine Learning Repository, 2007.
Yeh, I.-C., Yang, K.-J., and Ting, T.-M., Knowledge discovery on rfm model using bernoulli sequence. Expert Syst. Appl. 36(3):5866–5871, 2009.
Street, W. N., Wolberg, W. H., and Mangasaria, O. L., Nuclear feature extraction for breast tumor diagnosis. In: International Symposium on Electronic Imaging: Science and Technology, Vol. 1905, pp. 861–870, 1993.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-010-9472-5