A review of feature selection methods in medical applications

Remeseiro, Beatriz; Bolón-Canedo, Verónica

doi:10.1016/j.compbiomed.2019.103375

Cited by 568 publications

(332 citation statements)

References 71 publications

(61 reference statements)

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“…The machine learning algorithms used in this study were LASSO, linear SVM, RBF-SVM, RF, and KNN. Including irrelevant features in a machine learning model likely results in overfitting and can undermines the generalizability of a prediction model 38 . Thus, LASSO and SVM were regularized using L1-norm which automatically selects important features 39 .…”

Section: Methodsmentioning

confidence: 93%

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

Lim

Kim

et al. 2020

Sci Rep

165

121

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The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

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Section: Methodsmentioning

confidence: 93%

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

Lim

Kim

et al. 2020

Sci Rep

165

121

View full text Add to dashboard Cite

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“…This is crucial in radiomics, for some image features tend to be strongly correlated with one another [44]. Approaches to feature selection come in different varieties, such as correlation-based selection, reduction based on mutual information gain, recursive elimination, and Lasso regularization (see [45] for a recent review on this subject).…”

Section: Post Processingmentioning

confidence: 99%

PET/CT Radiomics in Lung Cancer: An Overview

et al. 2020

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Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.

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“…In many medical domain problems, such as medical imaging, biomedical signal processing, and DNA microarray data, the collected datasets usually contain very high feature dimensions. To deal with this high dimensionality problem, related literature have shown the positive effect of considering feature selection on various medical domain datasets (Huang et al, 2019; Huang, Chen, Lin, Ke, & Tsai, 2017; Remeseiro & Bolon‐Canedo, 2019; Shilaskar & Ghatol, 2013; Zhu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.

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A review of feature selection methods in medical applications

Cited by 568 publications

References 71 publications

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

PET/CT Radiomics in Lung Cancer: An Overview

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

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