Linked Life Data

19173975

Source:http://linkedlifedata.com/resource/pubmed/id/19173975

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2009-1-28
pubmed:abstractText
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.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0254-6450
pubmed:author
pubmed:issnType
Print
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1251-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
[Risk prediction model of perinatal congenital heart disease].
pubmed:affiliation
School of Public Health, Fujian Medical University, Fuzhou 350004, China.
pubmed:publicationType
Journal Article, English Abstract