Neuro-fuzzy classification of prostate cancer using NEFCLASS-J

Abstract Medical diagnosis has been the most proper area for the implementations of artificial intelligence for approximately 20 years. In this paper, a new approach based on neuro-fuzzy classification (NEFCLASS) tool has been presented to classify prostate cancer. The tool has the features of batch learning, automatic cross validation, automatic determination of the rule base size, and handling of missing values to increase its interpretability. We have investigated how good medical data analysis could be done with NEFCLASS-J, and what effects selected parameters have on classifier performances. Medical data were obtained from patients with real prostate cancer and benign prostatic hyperplasia (BPH). The reason for the selection of these two illnesses was the fact that their symptoms are very similar yet their differentiation is very crucial. The results showed that, for creating high performance of classifier appropriate for the data used, firstly it is necessary to decide well on the membership type and the number of fuzzy sets and then validation procedure. After a good classifier has been found, other parameters should be investigated to improve this classifier. In the light of this study, we can present a foresight for the diagnosis of the patients with prostate cancer or BPH.