Feature Selection in Healthcare Datasets: Towards a Generalizable Solution.

Journal: Computers in biology and medicine
Published Date: Jul 29, 2025

Abstract

BACKGROUND AND OBJECTIVE: The increasing dimensionality of healthcare datasets presents major challenges for clinical data analysis and interpretation. This study introduces a scalable ensemble feature selection (FS) strategy optimized for multi-biometric healthcare datasets aiming to: address the need for dimensionality reduction, identify the most significant features, improve machine learning models' performance, and enhance interpretability in a clinical context.

Authors

  • Ida Maruotto
    Institute of Biomedical and Neural Engineering, Reykjavik University, 102 Reykjavik, Iceland.
  • Federica Kiyomi Ciliberti
    Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
  • Paolo Gargiulo
    Institute of Biomedical and Neural Engineering, Department of Engineering, Reykjavik University, Reykjavik, Iceland; Department of Science, Landspitali University Hospital, Reykjavik, Iceland.
  • Marco Recenti
    Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland. Electronic address: marcor@ru.is.

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