文章基本信息

标题：Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based on Genetic Algorithm
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作者：Sidahmed Mokeddem ; Baghdad Atmani ; Mostéfa Mokaddem 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2013
卷号：3
期号：3
页码：41-51
DOI：10.5121/csit.2013.3305
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Feature Selection (FS) has become the focus of much research on decision support systems areas for which datasets with tremendous number of variables are analyzed. In this paper we present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Na.ve (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA generates in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final set of attribute contains the most relevant feature model that increases the accuracy. The algorithm in this case produces 85.50% classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is correspondingly compared with other FS algorithms. The Obtained results have shown very promising outcomes for the diagnosis of CAD
关键词：Bayes Na.ve;Best First Search;C4.5; Coronary Artery Disease; Feature Selection; Genetic ;Algorithm; Machine Learning; Multi-Layer Perceptron;Sequential Floating Forward Search;Support Vector machine