Risk prediction model of perinatal congenital heart disease

VernacularTitle:围产儿先天性心脏病危险度预测模型
Author: Li-Bo ZHOU ¹ ; Ling ZHENG ; Jia-You LUO ; Qi-Yun DU ; Jun-Qun FANG ; Zhen-Qiu SUN
Author Information

1. 福建医科大学
Keywords: Congenital heart disease; Logistic regression; Decision tree; Prediction model
From: Chinese Journal of Epidemiology 2008;29(12):1251-1254
CountryChina
Language:Chinese
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.