The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
A review of feature selection methods in medical applications
Open accessAbstract
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.
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A decision support system for multimodal brain tumor classification using deep learning
Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00321-0 · Cited by 305
A multiple combined method for rebalancing medical data with class imbalances
Wang, Y.-C., & Cheng, C.-H. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104527 · Cited by 66
Mitigating bias in clinical machine learning models
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., & Adedinsewo, D. (2024) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-023-01032-0 · Cited by 23
Diwan, S., Gandhi, V., Baidya Kayal, E., Khanna, P., & Mehndiratta, A. (2025) · Intensive Care Medicine Experimental · doi:10.1186/s40635-025-00765-5 · Cited by 17
Kishikawa, R., Kodera, S., Setoguchi, N., Tanabe, K., Kushida, S., Nanasato, M., Maki, H., Fujita, H., Kato, N., Watanabe, H., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae097 · Cited by 8
Metadata
- Authors
- Remeseiro, B., & Bolon-Canedo, V.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 112
- Pages
- 103375
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2965743638
- Cited by
- 804
- References
- 72
- FWCI
- 67.88
- Type
- journal article
- Language
- en
- Open access
- green
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