1- Dept of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Qom, Iran , j. rezaee@qom. ac. ir 2- Dept of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Qom, Iran 3- Dept of Computer Engineering, Faculty of Technology and Engineering, University of Esfehan, Esfehan, Iran
Abstract: (1175 Views)
Introduction: It is of utmost importance to predict cardiovascular diseases correctly. Therefore, it is necessary to utilize those models with a minimum error rate and maximum reliability. This study aimed to combine an artificial neural network with the genetic algorithm to assess patients with myocardial infarction and congestive heart failure.
Materials & Methods: This study utilized a multi-layer perceptron artificial neural network and a backpropagation algorithm combined with a genetic algorithm to assess the condition of two patients with cardiovascular diseases. The medical records of 497 patients with cardiovascular diseases at Ayatollah Golpayegani Hospital, Qom, Iran, were collected using a clustering sampling method. The data were analyzed using a Receiver Operating Characteristics Curve. Eventually, the data, including personal and clinical variables of patients (i.e., age, gender, dyspnea, blood pressure variations, and blood test results) were selected using sigmoid-transfer and tangent-sigmoid functions. Following that, the neural network was trained with 19 input neurons and 5 middle-layer neurons.
Findings: According to the results, a neural network with 5 middle-layer neurons has more precision, compared to other modes. Therefore, it is possible to predict myocardial infarction in the patients using this neural network with a minimum of 97.7% precision.
Discussion & Conclusions: An artificial neural network was combined with a genetic algorithm and proposed as a model to predict myocardial infarction in this study. Moreover, it was attempted to utilize important and cost-effective factors for cardiovascular diseases. As a result, the patients can be aware of their disease at the lowest cost.
Rezaeenoor J, Saadi G, Jahani M. Prediction of Cardiovascular Diseases Using
an Optimized Artificial Neural Network. sjimu. 2019; 27 (5) :15-23 URL: http://sjimu.medilam.ac.ir/article-1-5138-en.html