Multi-class f-score feature selection approach to classification of obstructive sleep apnea syndrome

Authors:
Salih Güneş;Kemal Polat;Şebnem Yosunkaya
Affiliations:
Selcuk University, Electrical and Electronics Engineering Department, 42035 Konya, Turkey;Selcuk University, Electrical and Electronics Engineering Department, 42035 Konya, Turkey;Selcuk University, Faculty of Medicine, Sleep Laboratory, Konya, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
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Abstract

In this paper, a new feature selection named as multi-class f-score feature selection is proposed for sleep apnea classification having different disorder degrees (mild OSAS, moderate OSAS, serious OSAS, and non-OSAS). f-Score is used to measure the discriminating power of features in the classification of two-class pattern recognition problems. In order to apply the f-score feature selection to multi-class datasets, we have used the f-score feature selection as pairwise (in the form of two classes) in the diagnosis of obstructive sleep apnea syndrome (OSAS) with four classes. After feature selection process, MLPANN (Multi-layer perceptron artificial neural network) classifier is used to diagnose the OSAS having different disorder degrees. While MLPANN obtained 63.41% classification accuracy on the diagnosis of OSAS, the combination of MLPANN and multi-class f-score feature selection achieved 84.14% classification accuracy using 50-50% training-testing split of OSAS dataset with four classes. These results demonstrate that the proposed multi-class f-score feature selection method is effective and robust in determining the disorder degrees of OSAS.