Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach
Automatic diagnosis of diseases always has been of interest as an interdisciplinary study amongst computer and medical science researchers. In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP neural network. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. Outcomes suggest the role of effective symptoms selection and the advantages of data fuzzificaton on a neural networks-based automatic medical diagnosis system. Employing ETM diseases as the case study, system eventually gets through the 97.5% of correct detection of abnormal cases.
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