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March 16, 20260 citationsOpen Access

Comparative Analysis of Feature Selection Methods in the use-case of ARDS Classification in Clinical Time-Series Data

SFSimon FonckSWSina WeddingSFSebastian Fritsch

Key Points

  • The research aims to compare the effectiveness of various feature selection methods for classifying Acute Respiratory Distress Syndrome (ARDS).
  • Investigated four feature selection methods: ?2, ANOVA F-test, Lasso, and a Tree-based method.
  • Utilized existing Random Forest algorithm for comparison.
  • Analyzed time-series data from intensive care unit patients.
  • Feature selection did not significantly enhance ARDS classification performance.
  • Using fewer features yielded results comparable to the complete dataset.
  • Indicates potential benefits of dimensionality reduction for ARDS classification.

Abstract

This study investigates four feature selection methods (?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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Cite This Study

Fonck et al. (2025) studied this question.

synapsesocial.com/papers/69b79e6e8166e15b153abb15https://doi.org/10.18416/automed.2026.2471
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