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Comparison of Different Statistical Prediction Methods for Fetal Growth Abnormalities

dc.contributor.authorZhang, Hongqun
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Scienceen_US
dc.contributor.departmentDepartment of Mathematics & Statistics - Statistics Divisionen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorHong Guen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerBruce Smithen_US
dc.contributor.thesis-readerHong Guen_US
dc.contributor.thesis-supervisorDavid Hamilton, Stefan Kuhleen_US
dc.date.accessioned2014-08-21T18:43:42Z
dc.date.available2014-08-21T18:43:42Z
dc.date.defence2014-08-19
dc.date.issued2014-08-21
dc.description.abstractThe decision tree method has become very popular because it can efficiently accommodate a large amount of data with a mixture of different types of variables, missing data and many irrelevant predictors. Trees also can be graphically presented and easily explained. However, the weaknesses of the decision tree model are high variance and lower predictive accuracy. These problems have been substantially improved by the tree ensemble-based methods: random forests and boosting trees. In this study, tree and tree ensemble-based methods, as well as logistic regression are reviewed and are applied to the Nova Scotia Atlee Perinatal Database to predict fetal growth abnormalities such as infants with birth weight small for gestational age or large for gestational age. It was found that predictive accuracy of the boosted tree model is better than both random forests and decision trees, but this model does not show much improvement over logistic regression.en_US
dc.identifier.urihttp://hdl.handle.net/10222/53985
dc.language.isoenen_US
dc.subjectfetal growth abnormalities; logistic regression; regression and classification trees; random forests; boosting trees.en_US
dc.titleComparison of Different Statistical Prediction Methods for Fetal Growth Abnormalitiesen_US

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