The problem of birth defects is one of the most important public health problems in the world, and the application of data mining method to improve the diagnostic accuracy for birth defects is a hot medical research issue.The authors proposed two emerging patterns for birth defects feature extraction: the defection contrast to normal and the normal contrast to defection.The Birth Defects Detection based on Emerging Patterns (BDD-EP) algorithm was implemented through combining the proposed patterns with CA.5 decision tree.The extensive experimental results show that the detection accuracy of BDD-EP is as high as 90.1％, the F-measure of normal samples is 93.9％, and the F-measure of defect samples is 74.1％.Compared with other famous classical classification algorithms, BDD-EP algorithm can get better results.%出生缺陷是目前世界各国关注的公共卫生问题,采用数据挖掘技术提高出生缺陷的诊断水平是当前数字医学的热点研究方向.为此,提出了适合出生缺陷特征提取的两种显露模式:有缺陷相比于无缺陷的显露模式和无缺陷相比于有缺陷的显露模式.将新模式与决策树C4.5算法结合,实现了基于显露模式的出生缺陷判别(BDD-EP)算法.实验结果表明BDD-EP算法判别准确率高达90.1%,判别正常类的F度量值为93.9%,判别缺陷类的,度量值为74.1%,均高于其他几种著名的分类算法的判别效果.
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