Purpose

To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.

Methods

The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data.

Results

Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models.

Conclusions

Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists’ counselling and their patients in adjusting the appropriate treatment strategy.

中文翻译：

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机器学习与用于预测 IVF 结果的经典统计数据。

目的

评估机器学习方法在预测 IVF 结果方面是否优于经典统计模型。

方法

研究人群包括 136 名女性，他们于 2014 年 1 月至 2016 年 8 月在大学附属的三级医疗中心接受了新的试管婴儿周期。我们测试了两种机器学习算法、支持向量机 (SVM) 和人工神经网络 (NN) 与经典统计数据（逻辑回归）相比预测 IVF 结果（取回的卵母细胞数量、成熟卵母细胞、优质胚胎、阳性β-hCG、临床妊娠和活产）基于年龄和BMI，有或没有临床数据。

结果

基于年龄、BMI 和临床特征的机器学习算法（SVM 和 NN）在预测提取的卵母细胞数量、成熟卵母细胞、受精卵母细胞、优质胚胎、阳性β-hCG、临床妊娠和活产方面取得了更好的性能，与逻辑回归模型相比。虽然 NN 和 SVM 的准确度分别为 0.69 到 0.9 和 0.45 到 0.77，但使用逻辑回归模型的准确度为 0.34 到 0.74。

结论

我们的研究结果表明，基于年龄、BMI 和临床数据的机器学习算法在预测 IVF 结果方面优于逻辑回归，因此可以帮助生育专家的咨询及其患者调整适当的治疗策略。