The nature of statistical learning theory
The nature of statistical learning theory
Variable selection using svm based criteria
The Journal of Machine Learning Research
Application of the GA/KNN method to SELDI proteomics data
Bioinformatics
Computers in Biology and Medicine
Information Sciences: an International Journal
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
Computers in Biology and Medicine
Pattern recognition using invariants defined from higher order spectra: 2-D image inputs
IEEE Transactions on Image Processing
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
An evolutionary approach for searching metabolic pathways
Computers in Biology and Medicine
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This paper proposes a congestive heart failure (CHF) recognition method that includes features calculated from the bispectrum of heart rate variability (HRV) diagrams and a genetic algorithm (GA) for feature selection. The roles of the bispectrum-related features and the GA feature selector are investigated. Features calculated from the subband regions of the HRV bispectrum are added into a feature set containing only regular time-domain and frequency-domain features. A support vector machine (SVM) is employed as the classifier. A feature selector based on genetic algorithm proceeds to select the most effective features for the classifier. The results confirm the effectiveness of including bispectrum-related features for promoting the discrimination power of the classifier. When compared with the other two methods in the literature, the proposed method (without GA) outperforms both of them with a high accuracy of 96.38%. More than 3.14% surpluses in accuracies are observed. The application of GA as a feature selector further elevates the recognition accuracy from 96.38% to 98.79%. When compared to the Isler and Kuntalp's impressive results recently published in the literature that also uses GA for feature selection, the proposed method (with GA) outperforms them with more than 2.4% surpass in the recognition accuracy. These results confirm the significance of recruiting bispectrum-related features in a CHF classification system. Moreover, the application of GA as feature selector can further improve the performance of the classifier.