Contribution to a conference proceedings/Contribution to a book

FZJ-2026-03428

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Comparative Analysis of Feature Selection Methods in the use-case of ARDS Classification in Clinical Time-Series Data

Fonck, S. (Corresponding author) ; Wedding, S. ; Fritsch, S.FZJ* ; Stollenwerk, A.

2026
Infinite Science Publishing

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Please use a persistent id in citations: doi:10.18416/AUTOMED.2026.2471  doi:10.34734/FZJ-2026-03428

Abstract: This study investigates four feature selection methods ($χ^2$, ANOVA F-test, Lasso, and a Tree-based method) to enhance Acute Respiratory Distress Syndrome (ARDS) classification in time-series intensive care unit data using an existing Random Forest algorithm. While feature selection did not significantly improve ARDS classification performance, using a reduced number of features achieved comparable results to using the entire dataset. This indicates the potential of dimensionality reduction for ARDS classification.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Center for Advanced Simulation and Analytics (CASA)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2026

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