developing an intelligent tool for breast cancer prognosis using artificial neural network

Today, there is ample scientific evidence that breast cancer (bc) is a global health challenge given its prevalence and invasive nature. therefore, early detection of bc can help minimize the devastating effects of the disease. this study aimed to design a clinical decision support system (cdss) based on the best artificial neural network (ann) configuration to identify patients quickly. using a single-center registry, we retrospectively reviewed the records of 3380 suspected bc cases. the independence test of chi-square at p<0.01 was utilized to select the most important criteria. then the different ann configuration was implemented in the matlab r2013 environment and compared using some evaluation criteria. finally, the best ann configuration was obtained. after implementing feature selection, 20 variables were determined as the most relevant factors. the experimental results indicate that the best performance was obtained by the 20-25-1 configuration with ppv=90.9%, npv=99.7%, sensitivity=98.9%, specificity=97.9%, accuracy=98.1%, and auc=0.958. the proposed software can identify cases of bc from healthy individuals with optimal diagnostic accuracy. additionally, it might be integrated as a practical and helpful tool in natural clinical settings for easy and effective disease screening