CAJ | 학술논문

通过对先天性心脏病(CHD)影响因素的分析,建立胎儿CHD危险度预测模型.采用单因素logistic回归分析筛选影响因素后用多因素非条件logistic回归和决策树法建立胎儿CHD危险度预测模型,分析比较两种预测方法的优势与不足.实例分析表明,logistic回归模型和决策树模型对215例训练样本和55例测试样本的分类正确率分别为80.93%、82.79%和85.45%、89.09%.将logistic回归和决策树方法联合应用,不仅能提高预测的准确率,还能克服因素间共线性的影响,从而保证分析的准确和完善.
통과대선천성심장병(CHD)영향인소적분석,건립태인CHD위험도예측모형.채용단인소logistic회귀분석사선영향인소후용다인소비조건logistic회귀화결책수법건립태인CHD위험도예측모형,분석비교량충예측방법 적우세여불족.실례분석표명,logistic회귀모형화결책수모형대215례훈련양본화55례측시양본적분류정학솔분별위80.93%、82.79%화85.45%、89.09%.장logistic회귀화결책수방법 연합응용,불부능제고예측적준학솔,환능극복인소간공선성적영향,종이보증분석적준학화완선.
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.