A Comparison of Performances of Different Feature Selection Methods applied to Biomedical Data

Abstract

Migraine is a debilitating disease whose causes are not yet completely explained. Near-InfraRed Spectroscopy (NIRS) is a non-invasive technology commonly used for the assessment of the cerebral autoregulation during active stimuli. Feature Selection (FS) allows dimensionality reduction of multivariate datasets, highlighting the most informative variables and deleting redundant and irrelevant information. Rough Set Theory (RST) is one of the most used tool for FS, enables to manage incomplete and imperfect knowledge without any assumption about data model. This study involved a total of 80 subjects, divided in 3 groups: 15 healthy subjects taken as controls, 14 women suffered from migraine without aura and 51 women from migraine with aura. We apply three different methods of FS based on RST to a set of 26 parameters extracted from NIRS signals recorded in the subjects during breath-holding (BH) and hyperventilation (HYP). We compare the extracted subsets of features in the subjects’ classification by means of Artificial Neural Networks. The results show good performance for all subsets, with a percentage of correct classification above the 90%