[Risk prediction model of perinatal congenital heart disease]

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.