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Cancer Diagnosis Using Modified Fuzzy Network

Author(s): Essam Al-Daoud

Journal: Universal Journal of Computer Science and Engineering Technology
ISSN 2219-2158

Volume: 1;
Issue: 2;
Start page: 73;
Date: 2010;
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Keywords: fuzzy c-means | radial basis functions | fuzzy-neuro | rules | cancer diagnosis.

In this study, a modified fuzzy c-means radial basis functions network is proposed. The main purposes of the suggested model are to diagnose the cancer diseases by using fuzzy rules with relatively small number of linguistic labels, reduce the similarity of the membership functions and preserve the meaning of the linguistic labels. The modified model is implemented and compared with adaptive neuro-fuzzy inference system (ANFIS). The both models are applied on "Wisconsin Breast Cancer" data set. Three rules are needed to obtain the classification rate 97% by using the modified model (3 out of 114 is classified wrongly). On the contrary, more rules are needed to get the same accuracy by using ANFIS. Moreover, the results indicate that the new model is more accurate than the state-of-art prediction methods. The suggested neuro-fuzzy inference system can be re-applied to many applications such as data approximation, human behavior representation, forecasting urban water demand and identifying DNA splice sites.
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