应用机器学习方法预测巨大儿

目的:应用机器学习方法,提高预测巨大儿的准确性。方法:查阅2015年1月至2016年12月我院产科巨大儿及正常体质量新生儿病历,获取新生儿出生体质量及超声测量数据。以新生儿实际出生体质量为金标准,评价并比较超声内置的Hadlock公式、多元线性回归以及k近邻、支持向量机、随机森林分类的机器学习方法对巨大儿预测的准确性。结果:内置的Hadlock公式预测巨大儿的灵敏度为40.86%,Youden指数为39.95%;多元线性回归预测巨大儿的灵敏度为60.22%,Youden指数为58.85%;机器学习法中k近邻预测巨大儿的灵敏度86.21%,Youden指数为75.10%;支持向量机预测巨大儿的灵敏度86.21%,Youden指数为73.51%;随机森林预测巨大儿的灵敏度81.03%,Youden指数为71.51%。多元线性回归模型方法的Youden指数大于超声内置公式方法,差异有统计学意义(u=3.64,P0.001);机器学习法中k近邻、支持向量机、随机森林分类的Youden指数大于超声内置公式的Youden指数(P0.001),k近邻、支持向量机的Youden指数大于多元线性回归模型法(P0.05)。结论:机器学习方法预测巨大儿的准确性较高,具有参考应用价值。

Application of Machine Learning to Predict Macrosomia

Objective: To improve the accuracy of prediction of macrosomia by application of machine learning.Methods: Ultrasound measurement data and fetal birth weight of macrosomia and normal birth weight neonates were collected during January 2015 to December 2016 in Mindong Hospital Affiliated to Fujian Medical University.Ultrasound built-in Hadlock formula,multiple linear regression,k-nearest neighbor,support vector machine,random forest were evaluated and compared to predict macrosomia using actual fetal birth weight as the gold standard.Results: The sensitivity of built-in Hadlock formula to predict macrosomia was 40. 86% and Youden index was39. 95%.The sensitivity of the multivariate linear regression was 60. 22% and the Youden index was 58. 85%.The sensitivity of the k-nearest neighbor was 86. 21% and the Youden index was 75. 10%.The sensitivity of the support vector machine was 86. 21% and the Youden index was 73. 51%.The sensitivity of the random forest was 81. 03% and the Youden index was 71. 51%.The Youden index of multivariate linear regression was significantly bigger than that of built-in Hadlock( u = 3. 64,P<0. 001).The Youden index of k-nearest neighbor,support vector machine and random forest was significantly bigger and built-in Hadlock and multivariate linear regression( P<0. 001,P<0. 05).Conclusions: The machine learning is of high accuracy and great value of application.