机器学习算法在预测臀先露外倒转术疗效中的应用研究

Application research of machine learning in prediction the effect of external cephalic version for breech presentation

目的探讨不同的机器学习算法在预测臀先露外倒转术疗效中的效能及其临床应用价值。方法收集2017年1月至2020年6月在南方医科大学附属东莞医院行臀先露外倒转术孕产妇的临床数据资料。分别采用朴素贝叶斯(NB)、随机森林(RF)、多层感知器(MLP)、逻辑回归(LR)、支持向量机(SVM)、梯度提升决策树(GBDT)6种机器学习算法,构建臀先露外倒转术疗效预测模型,以精确度、召回率、F1值、准确率和ROC曲线下面积作为评估指标。结果有效样本共316例,其中239例(75.6%)经外倒转术纠正胎位。MLP机器学习算法在测试集中综合反映精确率和召回率的F1值为0.92,ROC曲线下面积为0.816,均优于其他5种机器学习算法。结论MLP机器学习算法在预测臀先露外倒转术疗效中展现较好的效能,具有一定的临床应用潜力。

Objective To study the efficiency and clinical application value of different machine learning in predicting the effect of external cephalic version(ECV)for breech presentation.Methods Clinical data of pregnant women who underwent ECV for breech presentation from January 2017 to June 2020 at Dongguan Hospital of Southern Medical University were collected to build ECV prediction models using NB,RF,MLP,LR,SVM and GBDT respectively.Precision rate,recall rate,F1 index and area under the receiver operator characteristic curve(AUROC)were used to evaluate the predictive value.Results Among the 316 subjects included in this study,239(75.6%)cases underwent a successful ECV.The F1 index of MLP model which reflected precision rate and recall rate in the test set was 0.92,while the AUROC was 0.816,higher than the other four machine learning-based models.Conclusion MLP exhibits good performance in the prediction of the effect of ECV,and provides a clinical application potential.