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Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

Arabasadi, Z ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cmpb.2017.01.004
  3. Publisher: Elsevier Ireland Ltd , 2017
  4. Abstract:
  5. Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset. © 2017
  6. Keywords:
  7. Cardiovascular disease ; Coronary artery disease ; Genetic algorithm ; Neural network ; Cardiology ; Data mining ; Decision making ; Diagnosis ; Genetic algorithms ; Heart ; Learning systems ; Neural networks ; Cardio-vascular disease ; Causes of death ; Computer aided ; Highly accurate ; Hybrid neural network-genetic algorithm ; Initial weights ; Sensitivity and specificity ; Diseases ; Adult ; Aged ; Article ; Artificial neural network ; Back propagation ; Computer aided decision making ; Computer assisted diagnosis ; Coronary artery disease ; Diagnostic accuracy ; Female ; Gene mutation ; Human ; Major clinical study ; Male ; Support vector machine ; Algorithm ; Genetics ; Heart Diseases ; Algorithms ; Humans ; Neural Networks (Computer) ; Support Vector Machine
  8. Source: Computer Methods and Programs in Biomedicine ; Volume 141 , 2017 , Pages 19-26 ; 01692607 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0169260716309695