Background:Early prevention of Spontaneous Abortion(SA)is essential for the treatment of recurrent spontaneous abortion.Objective:In this retrospective study,a variety of machine learning methods were used to develop ...Background:Early prevention of Spontaneous Abortion(SA)is essential for the treatment of recurrent spontaneous abortion.Objective:In this retrospective study,a variety of machine learning methods were used to develop predictive models and diagnose the potential risk of SA.Methods:A total of 663 pregnant women participated in the case-control study,586 of which were SA patients and 77 were normal parturition women.The research data included 25 features of Traditional Chinese Medicine(TCM)constitution and clinical data related to SA.This work utilized 8 machine learning techniques including logistic regression,gradient boosting decision tree,k-nearest neighbor,classification and r-egression tree,multilayer perceptron,support vector machine,random forest and XG-Boost to predict SA.The performances of the applied models were evaluated by using the method of 10-fold cross-validation and by computing the diagnostic test characteristics,including accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve.Results:The𝐹1 scores of these eight machine learning techniques were all above 97.5%.Among them,gradient boosting decision tree had the best prediction result on SA.The accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve of gradient boosting decision tree were 97.9%,99%,98.6%,98.8%,and 97.3%,respectively.Conclusion:The paper has accurately predicted the risk of SA combined with TCM constitution and clinical data.展开更多
文摘Background:Early prevention of Spontaneous Abortion(SA)is essential for the treatment of recurrent spontaneous abortion.Objective:In this retrospective study,a variety of machine learning methods were used to develop predictive models and diagnose the potential risk of SA.Methods:A total of 663 pregnant women participated in the case-control study,586 of which were SA patients and 77 were normal parturition women.The research data included 25 features of Traditional Chinese Medicine(TCM)constitution and clinical data related to SA.This work utilized 8 machine learning techniques including logistic regression,gradient boosting decision tree,k-nearest neighbor,classification and r-egression tree,multilayer perceptron,support vector machine,random forest and XG-Boost to predict SA.The performances of the applied models were evaluated by using the method of 10-fold cross-validation and by computing the diagnostic test characteristics,including accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve.Results:The𝐹1 scores of these eight machine learning techniques were all above 97.5%.Among them,gradient boosting decision tree had the best prediction result on SA.The accuracy,precision,recall,𝐹1 score,and the AUC of ROC curve of gradient boosting decision tree were 97.9%,99%,98.6%,98.8%,and 97.3%,respectively.Conclusion:The paper has accurately predicted the risk of SA combined with TCM constitution and clinical data.
