title: [Risk prediction model of perinatal congenital heart disease].
abstract: Through analyzing the influencing factors of congenital heart disease (CHD), it is aimed to establish CHD risk prediction model in fetus, and simultaneously provide theoretical foundation for CHD prevention. One-factor logistic regression method was used to screen the significant factors regarding CHD, and to separately adopt multiple-factor non-conditional logistic regression method and decision tree to set up model prediction fetus CHD risk and to analyze the advantages and shortcomings. Correct classification rates turned to be 80.93% and 82.79% respectively among 215 'training samples' by the two methods and the rates were 85.45% and 89.09% respectively among 55 'testing samples'. The alliance of logistic regression and decision tree can overcome influence by co-linearity to guarantee the accuracy and perfection, as well as promoting the predictive accuracy.
authors.affiliation: School of Public Health, Fujian Medical University, Fuzhou 350004, China.
authors.forename: Li-bo
authors.initials: LB
authors.lastname: Zhou
authors.forename: Ling
authors.initials: L
authors.lastname: Zheng
authors.forename: Jia-you
authors.initials: JY
authors.lastname: Luo
authors.forename: Qi-yun
authors.initials: QY
authors.lastname: DU
authors.forename: Jun-qun
authors.initials: JQ
authors.lastname: Fang
authors.forename: Zhen-qiu
authors.initials: ZQ
authors.lastname: Sun
mesh_terms: D003663:Decision Trees; D005260:Female; D006330:Heart Defects, Congenital / Q000453:epidemiology*; D006801:Humans; D007231:Infant, Newborn; D016015:Logistic Models; D011247:Pregnancy; D018570:Risk Assessment / Q000379:methods