The nature of statistical learning theory
The nature of statistical learning theory
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
Expert Systems with Applications: An International Journal
Identification of ischemic heart disease via machine learning analysis on magnetocardiograms
Computers in Biology and Medicine
Computerized screening of children congenital heart diseases
Computer Methods and Programs in Biomedicine
Bio-medical entity extraction using support vector machines
Artificial Intelligence in Medicine
Information Sciences: an International Journal
Hybrid PSO and GA for neural network evolutionary in monthly rainfall forecasting
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Computers and Electronics in Agriculture
Computer Methods and Programs in Biomedicine
Linear and nonlinear analysis of normal and CAD-affected heart rate signals
Computer Methods and Programs in Biomedicine
Combining technical trading rules using particle swarm optimization
Expert Systems with Applications: An International Journal
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
The aim of this study is to search the efficiency of binary particle swarm optimization (BPSO) and genetic algorithm (GA) techniques as feature selection models on determination of coronary artery disease (CAD) existence based upon exercise stress testing (EST) data. Also, increasing the classification performance of the classifier is another aim. The dataset having 23 features was obtained from patients who had performed EST and coronary angiography. Support vector machine (SVM) with k-fold cross-validation method is used as the classifier system of CAD existence in both BPSO and GA feature selection techniques. Classification results of feature selection technique using BPSO and GA are compared with each other and also with the results of the whole features using simple SVM model. The results show that feature selection technique using BPSO is more successful than feature selection technique using GA on determining CAD. Also with the new dataset composed by feature selection technique using BPSO, this study reached more accurate values of success on CAD existence research with more little complexity of classifier system and more little classification time compared with whole features used SVM.