Analysis and Evaluation of Techniques for Myocardial InfarctionBased on Genetic Algorithm and Weight by SVM

سال انتشار: 1395
محل انتشار: فصلنامه سیستم های اطلاعاتی و مخابرات، دوره: 4، شماره: 2
کد COI اختصاصی: JR_JIST-4-2_006
زبان مقاله: انگلیسی
تعداد مشاهده: 379

نویسندگان

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

چکیده

Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by SVM. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, KNearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization.

کلیدواژه ها

Artificial Neural Network; Sequential Minimal Optimization; REPTree; Knowledge Discovery in Databases; Myocardial Infarction

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