Expert system based on neuro-fuzzy rules for diagnosis breast cancer

Research highlights? In this study, we have developed an expert system for diagnosing of breast cancer. ? Inference engine of Ex-DBC system includes neuro fuzzy rules. ? Ex-DBC system has 97% specificity, 96% positive and 81% negative predictive values. ? Ex-DBC can be used as a strong diagnostic tool for diagnosing of breast cancer. Recent advances in the field of artificial intelligence have led to the emergence of expert systems for medical applications. Moreover, in the last few decades computational tools have been designed to improve the experiences and abilities of physicians for making decisions about their patients.Breast cancer is the commonest cancer in women and is the second leading cause of cancer death (Jemal et al., 2003). Although it is curable when detected early, about one third of women with breast cancer die of the disease (Scheidhauer, Walter, & Seemann, 2004). In this study, we have developed an expert system that we called as an Ex-DBC (Expert system for Diagnosis of Breast Cancer), because differentiating between benign and malignant mammographic findings, however, is quite difficult. Only 15-30% of biopsies performed on nonpalpable but mammographically suspicious lesions prove malignant (Hall, Storella, Silverstone, & Wyshak, 1988). The golden standard for diagnosis of breast cancer is biopsy. But, biopsy can be a source of patient discomfort, bleeding and infection, and can burden the health care system with extra costs. Thus, to reduce unnecessary biopsy rate have acquired big importance.The fuzzy rules which will be use in inference engine of Ex-DBC system were found by using neuro-fuzzy method. Ex-DBC can be used as a strong diagnostic tool with 97% specificity, 76% sensitivity, 96% positive and 81% negative predictive values for diagnosing of breast cancer. That the developed system's positive predictive is high is very important. By means of this system can be prevented unnecessary biopsy. Beside it can be benefited from this system for training of students in medicine.

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