Representation and semiautomatic acquisition of medical knowledge in CADIAG-1 and CADIAG-2
Computers and Biomedical Research
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Expert Systems
Introduction to Expert Systems
Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics)
Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics)
Knowledge discovery on RFM model using Bernoulli sequence
Expert Systems with Applications: An International Journal
Fuzzy relation based modeling for medical diagnostic decision support: Case studies
International Journal of Knowledge-based and Intelligent Engineering Systems
Toxopert-I: knowledge-based automatic interpretation of serological tests for toxoplasmosis
Computer Methods and Programs in Biomedicine
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions
Journal of Medical Systems
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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.