Data pre-processing

Curse of dimensionality

Machine learning

Data mining

Philosophy

Feature selection

Pattern recognition (psychology)

Artificial intelligence

Feature (linguistics)

Preprocessor

Intrusion detection system

Computer science

Linguistics

Feature extraction

Dimensionality reduction

Selection (genetic algorithm)

Computers in Biology and Medicine

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.

A review of feature selection methods in medical applications