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- Title
Application of Artificial Neural Network for Assessing Coronary Artery Disease.
- Authors
Thohamtan, Reza Ali Mohammadpour; Esmaeili, Mohammad Hadi; Ghaemian, Ali; Esmaeili, Javad
- Abstract
Background and purpose: Since the human health is an essential issue in medical sciences, accurate predicting the individuals based on the disease status is of great importance. Therefore, models whose prediction ability yields to the minimum error and maximum certainty should be used. Thus, this study used artificial neural network model for predicting coronary artery disease (CAD) because it is believed that in comparison with other models it a more precise model. Materials and methods: Multilayer perceptron (MLP) with error back propagation algorithm (EBP) for assessing the coronary artery disease was implemented among 150 patients admitted in 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. At the end, the model correctly categorized some healthy individuals who didn't 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 treatments. Moreover, due to high sensitivity, it can diagnose the patients who really need such diagnostic treatments.
- Subjects
DIAGNOSIS; CORONARY disease; ARTIFICIAL neural networks; SYSTEMS biology; PERCEPTRONS; ANGIOGRAPHY; ALGORITHMS
- Publication
Journal of Mazandaran University of Medical Sciences (JMUMS), 2012, Vol 22, Issue 86, p8
- ISSN
1735-9260
- Publication type
Article