Conditional Mutual Information-based Feature Selection for Sex Differences Characterization

This study proposes a feature selection approach exploiting Conditional Mutual Information to identify the most relevant features to perform sex classification. The approach applied to features extracted from cardiovascular time series, is combined with a Linear Discriminant Analysis classifier. The feature selection method allowed to noticeably reduce the number of used features, achieving at the same time comparable and acceptable accuracy ($\sim \mathbf{6 2 \%}$) and overall good recall and F1scores for females ($\sim 71 \%$ and $\sim 63 \%$, respectively).