2024
Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022
Fang W, Liu Y, Xu C, Luo X, Wang K. Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022. International Journal Of Environmental Research And Public Health 2024, 21: 1474. PMID: 39595741, PMCID: PMC11594230, DOI: 10.3390/ijerph21111474.Peer-Reviewed Original ResearchConceptsSupport vector machineFeature selectionMachine learningRandom forestCollection of featuresMachine learning approachImbalance dataF1 scoreVector machineML techniquesLearning approachML toolsRelevant featuresPatient Health Questionnaire-4E-cigarette useML modelsML approachesRF algorithmRandom oversampling examplesMachineAlgorithmE-cigarettesSelection operatorLogistic regressionHealth Information National Trends Survey
2021
A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures
Boss J, Rix A, Chen Y, Narisetty N, Wu Z, Ferguson K, McElrath T, Meeker J, Mukherjee B. A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures. Environmetrics 2021, 32 PMID: 34899005, PMCID: PMC8664243, DOI: 10.1002/env.2698.Peer-Reviewed Original ResearchGroup least absolute shrinkageEnvironmental health studiesHealth outcomesHealth StudyLIFECODES birth cohortBirth cohortExposure interactionsPenalized regression methodsDose-response relationshipExposure mixturesComprehensive R Archive NetworkInteraction effectsInduce sparsityAdaptive weightsGroup lassoSelection operatorHeredity constraintLeast absolute shrinkageSelection frameworkNonlinear interaction effectsSample sizeVariable selectionJoint effectsCoefficient estimatesGroup structure
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