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Conditional Mutual Information-based Feature Selection for Sex Differences Characterization

Year: 2024

Type: conference-paper

Abstract: 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).

Source: Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

DOI: 10.1109/esgco63003.2024.10766964

Authors: Marta Iovino, Ivan Lazic, Chiara Barà, Luca Faes, Riccardo Pernice

Institutions: University of Palermo, University of Novi Sad

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: European Social Fund