中国卫生统计
中國衛生統計
중국위생통계
CHINESE JOURNAL OF HEALTH STATISTICS
2009年
5期
473-476
,共4页
方俊群%罗家有%姚宽保%曾春林%方超英%胡茹珊%杜其云%吴虹
方俊群%囉傢有%姚寬保%曾春林%方超英%鬍茹珊%杜其雲%吳虹
방준군%라가유%요관보%증춘림%방초영%호여산%두기운%오홍
出生缺陷%影响因素%决策树%预测模型
齣生缺陷%影響因素%決策樹%預測模型
출생결함%영향인소%결책수%예측모형
Birth defects%Influencing factor%Decision Tree%Forecasting model
目的 介绍决策树法的原理及其在出生缺陷预测中的应用,为出生缺陷研究提供一种新的思路.方法 通过1:2匹配的病例对照研究探讨湖南省前10位出生缺陷发生的影响因素;对单因素logistic回归分析中有统计学意义的变量采用C5.0决策树算法和判别分析构建预测模型.统计分析软件采用Clementine 11.0和SPSS 15.0.结果 决策树分类结果与实际类别的符合率为83.7%,灵敏度为74.1%,特异度为88.6%;判别分类与实际类别的符合率为64.7%,灵敏度为54.0%;特异度为70.3%.C5.0决策树法比判别分析法具有更好的预测效果,其判断准确率高于判别分析.结论 C5.0决策树法构建的出生缺陷预测模型,可获得比传统的判别分析更好的预测效果.通过建立孕妇资料数据库,结合专业知识选取高质量的指标,应用决策树法能够对出生缺陷的发生起到较好的预测作用.
目的 介紹決策樹法的原理及其在齣生缺陷預測中的應用,為齣生缺陷研究提供一種新的思路.方法 通過1:2匹配的病例對照研究探討湖南省前10位齣生缺陷髮生的影響因素;對單因素logistic迴歸分析中有統計學意義的變量採用C5.0決策樹算法和判彆分析構建預測模型.統計分析軟件採用Clementine 11.0和SPSS 15.0.結果 決策樹分類結果與實際類彆的符閤率為83.7%,靈敏度為74.1%,特異度為88.6%;判彆分類與實際類彆的符閤率為64.7%,靈敏度為54.0%;特異度為70.3%.C5.0決策樹法比判彆分析法具有更好的預測效果,其判斷準確率高于判彆分析.結論 C5.0決策樹法構建的齣生缺陷預測模型,可穫得比傳統的判彆分析更好的預測效果.通過建立孕婦資料數據庫,結閤專業知識選取高質量的指標,應用決策樹法能夠對齣生缺陷的髮生起到較好的預測作用.
목적 개소결책수법적원리급기재출생결함예측중적응용,위출생결함연구제공일충신적사로.방법 통과1:2필배적병례대조연구탐토호남성전10위출생결함발생적영향인소;대단인소logistic회귀분석중유통계학의의적변량채용C5.0결책수산법화판별분석구건예측모형.통계분석연건채용Clementine 11.0화SPSS 15.0.결과 결책수분류결과여실제유별적부합솔위83.7%,령민도위74.1%,특이도위88.6%;판별분류여실제유별적부합솔위64.7%,령민도위54.0%;특이도위70.3%.C5.0결책수법비판별분석법구유경호적예측효과,기판단준학솔고우판별분석.결론 C5.0결책수법구건적출생결함예측모형,가획득비전통적판별분석경호적예측효과.통과건립잉부자료수거고,결합전업지식선취고질량적지표,응용결책수법능구대출생결함적발생기도교호적예측작용.
Objective To introduce the principle of Decision Tree and its application in the forecasting of birth defects, and provide a new way in the study of birth defects. Methods 1:2 matched case-control study was used to explore the influencing factor of ten top birth defects in Hunan. Decision Tree C5.0 algorithm and discriminate analysis were used to construct forecast models. Clementine 11.0 and SPSS 15. 0 were used for statistical analysis. Results The coincident rate of Decision Tree categories with actual categories was 83.7% ;the sensitivity was74.1 %;the specificity was 88.6%. The coincident rate of discriminate categories with actual categories was 64.7% ;the sensitivity was 54.0% ;the specificity was 70.3%. Compared with discriminate analysis,Decision Tree methods had a better forecasting precision for its higher accuracy rate. Conclusion Compared with the conventional statistic method, Decision Tree methods had better forecasting precision. Decision Tree method is a very good forecasting model to predict birth defects.