Güneş, SalihPolat, KemalYosunkaya, Şebnem2020-03-262020-03-262010Güneş, S., Polat, K., Yosunkaya, Ş., (2010). Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome. Expert Systems with Applications, 37(2), 998-1004. DOI: 10.1016/j.eswa.2009.05.0750957-4174https://dx.doi.org/10.1016/j.eswa.2009.05.075https://hdl.handle.net/20.500.12395/25088In 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.en10.1016/j.eswa.2009.05.075info:eu-repo/semantics/openAccessObstructive Sleep Apnea Syndrome (OSAS)Multi-Class F-Score Feature SelectionMulti-Layer Perceptron Artificial Neural NetworkPolysomnographyArticle3729981004Q1WOS:000272432300012Q1