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Prediction of Cardiovascular Diseases Using
an Optimized Artificial Neural Network
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Jalal Rezaeenoor *1    , Ghofran Saadi2 , Meysam Jahani3 |
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 |
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Abstract: (4304 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.
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| Keywords: Artificial neural network, cardiovascular disease, Data mining, Genetic algorithm, Prediction |
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Full-Text [PDF 854 kb]
(2484 Downloads)
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Type of Study: Research |
Subject:
immunology
Received: 2018/08/19 | Accepted: 2019/09/15 | Published: 2019/12/31
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