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dc.contributor.authorFarideh Bagherzadeh-Khiabani
dc.contributor.authorAzra Ramezankhani
dc.contributor.authorFereidoun Azizi
dc.contributor.authorFarzad Hadaegh
dc.contributor.authorEwout W Steyerberg
dc.contributor.authorDavood Khalili
dc.date.accessioned2017-10-24T11:29:04Z
dc.date.available2017-10-24T11:29:04Z
dc.date.issued2016-Mar
dc.identifier.issn1878-5921
dc.identifier.urihttp://dsp.sbmu.ac.ir/xmlui/handle/123456789/76751
dc.description.abstractIdentifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach.
dc.sourceJournal of clinical epidemiology
dc.titleA tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results.
dc.identifier.doi10.1016/j.jclinepi.2015.10.002


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