ArticleInternational journal of medical informatics2018
Comparison of variable selection methods for clinical predictive modeling.
Article in International journal of medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 4 of them syntheses that pooled it.
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Who cites it
117 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Patient centred variables with univariate associations with unplanned ICU admission: a systematic review.BMC medical informatics and decision making · 2019Pooled it
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- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Prevalence of hypertension and associated factors among sanitation workers: a community based cross-sectional survey in five urban regions of Nepal.BMC cardiovascular disorders · 2026Article
- Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.BMC medical informatics and decision making · 2026Article
- Explainable machine learning for hypertension prevalence classification: a cross-sectional study in Hainan Province, China.Frontiers in cardiovascular medicine · 2026Article
- An individualized prognostic nomogram integrating clinical and pathological features in pediatric IgA vasculitis nephritis.Frontiers in medicine · 2026Article
- Glycaemic Status Modifies the Association Between Cardiometabolic Index and Cardio-Kidney Outcomes: A Multi-Cohort Analysis.Diabetes/metabolism research and reviews · 2026Article
- Development and validation of a population-based prediction model for prevalent hypertension: evidence from Qatar biobank.Frontiers in cardiovascular medicine · 2026Article
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- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
57 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
objectiveModern 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
methodsWe 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.
resultsIn 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.