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Comparison of variable selection methods for clinical predictive modeling

Content Provider	Scilit
Author	Sanchez-Pinto, Lazaro Nelson Venable, Laura Ruth Fahrenbach, John Churpek, Matthew M.
Copyright Year	2018
Description	Journal: International Journal of Medical Informatics Modern machine learning-based modeling methods are increasingly applied to clinical problems. One such application is in variable selection methods for predictive modeling. However, there is limited research comparing the performance of classic and modern for variable selection in clinical datasets. We analyzed the performance of eight different variable selection methods: four regression-based methods (stepwise backward selection using p-value and AIC, Least Absolute Shrinkage and Selection Operator, and Elastic Net) and four tree-based methods (Variable Selection Using Random Forest, Regularized Random Forests, Boruta, and Gradient Boosted Feature Selection). We used two clinical datasets of different sizes, a multicenter adult clinical deterioration cohort and a single center pediatric acute kidney injury cohort. Method evaluation included measures of parsimony, variable importance, and discrimination. In the large, multicenter dataset, the modern tree-based Variable Selection Using Random Forest and the Gradient Boosted Feature Selection methods achieved the best parsimony. In the smaller, single-center dataset, the classic regression-based stepwise backward selection using p-value and AIC methods achieved the best parsimony. In both datasets, variable selection tended to decrease the accuracy of the random forest models and increase the accuracy of logistic regression models. The performance of classic regression-based and modern tree-based variable selection methods is associated with the size of the clinical dataset used. Classic regression-based variable selection methods seem to achieve better parsimony in clinical prediction problems in smaller datasets while modern tree-based methods perform better in larger datasets.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003624/pdf
Ending Page	17
Page Count	8
Starting Page	10
ISSN	13865056
DOI	10.1016/j.ijmedinf.2018.05.006
Journal	International Journal of Medical Informatics
Volume Number	116
Language	English
Publisher	Elsevier BV
Publisher Date	2018-08-01
Access Restriction	Open
Subject Keyword	Journal: International Journal of Medical Informatics Medical Informatics Key Words Models, Statistical Regression Analysis Machine Learning Data Interpretation, Statistical Electronic Health Records Variable Selection
Content Type	Text
Resource Type	Article
Subject	Health Informatics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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