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Comparison of variable selection methods for clinical predictive modeling.

Abstract：

OBJECTIVE: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. MATERIALS AND METHODS: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. RESULTS: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. CONCLUSIONS: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.

journal_name

Int J Med Inform

journal_title

International journal of medical informatics

authors

Sanchez-Pinto LN,Venable LR,Fahrenbach J,Churpek MM

doi

10.1016/j.ijmedinf.2018.05.006

subject

Has Abstract

pub_date

2018-08-01 00:00:00

pages

10-17

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(18)30581-1

journal_volume

116

pub_type

杂志文章

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