Designing and evaluation of a decision support system for prediction of coronary artery disease

Introduction: Since human health is the issue of Medical Research, correct prediction of results is of a high importance. This study applies probabilistic neural network (PNN) for predicting coronary artery disease (CAD), because the PNN is stronger than other methods. Methods: In this descriptive-analytic study, The PNN method was implemented on ۱۵۰ patients admitted to the Mazandaran Heart Center, sari. For designing the network, ۸۰% of the data were used for stage of network training, and the remained ۲۰% were used for stage of network testing. In order to implement the network, facilities and functions existing in MATLAB ۷.۱۲.۰ were used and simulation was conducted in a PC with configurations of corei۵ CPU, ۲GHz processor, ۴GB ram, under operating system of Windows ۷. Results: After ۵ times simulation and comparison of the models produced, sensitivity and specificity rates obtained were ۱ and ۱. In the end, model correctly categorized some healthy subjects who did not need angiography and the treatment related to coronary artery disease. Conclusion: Due to the high specificity index, this model prevents side effects of angiography in patients who don't need such treatments. Moreover, due to high sensitivity, it can diagnose the patients who really need such diagnostic measures.