Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A review of feature selection techniques in bioinformatics
Bioinformatics
Survival prediction in patients undergoing ischemic cardiopathy
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Comparison of feature selection methods in ECG signal classification
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
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This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity.