A review of feature selection methods in medical applications

By Beatriz Remeseiro, Verónica Bolón-Canedo. Comput. Biol. Medicine, 2019

692 citations recorded by the source corpus.

Feature selection is a preprocessing technique that identifies the key features of a given problem. It has traditionally been applied in a wide range of problems that include biological data processing, finance, and intrusion detection systems. In particular, feature selection has been successfully used in medical applications, where it can not only reduce dimensionality but also help us understand the causes of a disease. We describe some basic concepts related to medical applications and provide some necessary background information on feature selection. We review the most recent feature selection methods developed for and applied in medical problems, covering prolific research fields such as medical imaging, biomedical signal processing, and DNA microarray data analysis. A case study of two medical applications that includes actual patient data is used to demonstrate the suitability of applying feature selection methods in medical problems and to illustrate how these methods work in real-world scenarios.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/064d5e3520ee0681bcc0adf07c32c72ecfbe43f8

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In Molecular Biology

Citations in the corpus

A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “Thus, wrapper methods identify the best-performing set of features for the chosen classifier algorithm (Guyon and Elisseeff, 2003; Remeseiro and Bolon-Canedo, 2019).”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “Remeseuro and Bolon-Canedo [3] reviewed feature selection methods on medical problems.”