Construction of a pregnancy prediction model in acupuncture treatment for diminished ovarian reserve based on machine learning  

基于机器学习构建针刺治疗卵巢储备功能减退的妊娠预测模型

作　　者：Ming-hui GOU Hui-sheng YANG Yi-gong FANG 勾明会;杨会生;房繄恭(中国中医科学院针灸医院,北京100700;首都医科大学附属北京妇产医院,北京100700;中国中医科学院针灸研究所,北京100010)

机构地区：[1]Acupuncture and Moxibustion Hospital,China Academy of Chinese Medical Sciences,Beijing 100700,China [2]Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,Beijing 100700,China [3]Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University,Beijing 100010,China

出　　处：《World Journal of Acupuncture-Moxibustion》2025年第1期32-40,共9页世界针灸杂志(英文版)

基　　金：Supported by the Qihuang Scholars Program in 2021;14th Five-Year National Key R&D Program Project:2022YFC3500504。

摘　　要：Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.

关 键 词：Machine learning ACUPUNCTURE Diminished ovarian reserve Pregnancy outcomes Prediction model 

分 类 号：R24[医药卫生—中医临床基础]