Protecting female fertility stands as a central goal and vision in a fertility-friendly society, and fallopian tube recanalization offers the possibility of pregnancy for patients with tubal infertility.

This study aims to accurately identify the influencing factors affecting successful pregnancy after fallopian tube recanalization and explore the application of the random forest algorithm in screening and predicting pregnancy influencing factors in such patients.

The study collected and analyzed data from 170 patients who underwent laparoscopic combined with hysteroscopic fallopian tube recanalization at Capital Medical University Affiliated Beijing Shijitan Hospital between 2016 and 2018. Based on whether the patients achieved successful natural pregnancy within 2 years after the surgery, they were divided into the pregnancy and non-pregnancy groups. Using the R software, a random forest model for predicting pregnancy risk after tube recanalization was established on the training data set (108 cases, 63.2% of cases, extracted via Bootstrap method), and its prediction accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were evaluated on the verification data set.

The study comprised 82 cases in the pregnancy group and 88 in the non-pregnancy group, with a spontaneous pregnancy rate of 48.2% post-surgery. The random forest algorithm, trained on the training set, demonstrated robust predictive capability upon validation, with an accuracy of 87.1%, sensitivity of 93.1%, specificity of 81.8%, positive predictive value of 81.8%, negative predictive value of 93.1%, and an AUC of 0.921. The random forest algorithm was employed to rank the importance of factors influencing pregnancy following fallopian tube recanalization by using variable importance scores. The analysis identified the top three significant predictor variables: duration of infertility, history of previous pregnancies, and patient age.

The random forest algorithm emerges as a viable tool for predicting factors influencing pregnancy after fallopian tube recanalization. The predictive model, predicated on infertility duration, history of prior pregnancies, and age, exhibits notable discrimination and accuracy. Early identification of key factors post-recanalization allows for timely and effective interventions. We recommend that patients presenting risk factors consider utilizing assisted reproductive technology to improve pregnancy rates.

保护女性的生育力是建立生育友好型社会的目标及愿景之一，其中输卵管再通术为输卵管性不孕患者妊娠提供了可能。

准确识别影响输卵管再通术后患者成功妊娠的影响因素，探讨随机森林算法在输卵管再通术后妊娠影响因素的筛选和预测中的应用效果。

回顾性收集2016—2018年于首都医科大学附属北京世纪坛医院妇科进行的宫腹腔镜联合下输卵管再通术170例患者的病例资料，根据术后患者2年内是否成功自然妊娠分为妊娠组与未妊娠组。采用R软件在训练集[采用Bootstrap自助法抽取63.2%（108例）数据]上建立输卵管再通术后妊娠风险预测的随机森林模型，在测试集上采用预测准确度、灵敏度、特异度、阳性预测值、阴性预测值和受试者工作特征曲线下面积（AUC）评价模型的预测效果。

妊娠组82例，未妊娠组88例，术后自然妊娠率为48.2%。通过随机森林算法对训练集建立的模型在测试集上验证，得出预测准确度为87.1%、灵敏度为93.1%、特异度为81.8%、阳性预测值为81.8%、阴性预测值为93.1%、AUC为0.921。采用随机森林算法通过变量重要性评分对输卵管再通术后妊娠影响因素的重要程度进行排序，得到排名前3位的重要预测变量为：不孕时间、既往妊娠次数及年龄。

随机森林算法可用于输卵管再通术后妊娠影响因素的风险预测，基于不孕时间、既往妊娠次数及年龄建立的预测模型具有较高的区分度和准确度。识别输卵管再通术后的关键因素，进行及时、有效的干预，建议合并危险因素的患者借助辅助生殖技术提高妊娠率。