کاربرد شبکه عصبی مصنوعی جهت ارزیابی بیماری عروق کرونری قلب

Background and purpose: Since the human health is an essential issue in medical sciences, accurate predicting the individual's disease status is of great importance. Therefore, predicting with models minimum error and maximum certainty should be used. This study used artificial neural network model for predicting coronary artery disease (CAD) because it is more precise Comared to after models. Materials and methods: Multilayer perceptron (MLP) with post propagation error algorithm (EBP) for assessing the coronary artery disease was implemented on 150 patients admitted to the Mazandaran Heart Center, Sari. Then, based on the 80% of the available data, an artificial neural network with NN (14, 12, 1), sigmoid transfer function and 1500 epochs were designed and trained. The data were fed into Excel program and then softwares for artificial neural network designing such as Pythia-Neural Network were employed. Results: Mean square of the error in training step was decreased to the level of 0.0238 and sensitivity and specificity rates obtained were 0.96 and 1. In the end, the model correctly categorized some healthy individuals who did not require angiography and the treatment related to coronary artery diseases. Conclusion: Due to the high specificity index, this model prevents side effects of angiography in patients who do not require such interventions. Moreover, due to high sensitivity, it can diagnose the patients who really need such diagnostic measures.