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. 2020 Oct;37(10):2405-2412.
doi: 10.1007/s10815-020-01908-1. Epub 2020 Aug 11.

Machine learning vs. classic statistics for the prediction of IVF outcomes

Zohar Barnett-Itzhaki  1   2   3   4 , Miriam Elbaz  5 , Rachely Butterman  5 , Devora Amar  5 , Moshe Amitay  5 , Catherine Racowsky  6 , Raoul Orvieto  7   8 , Russ Hauser  9 , Andrea A Baccarelli  10 , Ronit Machtinger  7   8
Affiliations

Machine learning vs. classic statistics for the prediction of IVF outcomes

Zohar Barnett-Itzhaki et al. J Assist Reprod Genet. 2020 Oct.

Abstract

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.

Keywords: IVF; Implantation; Machine learning; Oocytes; Prediction models.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Comparisons of intermediate IVF outcomes among the three models using BMI, age, and clinical data (n = 136). a. F1 score comparisons. b. Accuracy comparisons. LR, logistic regression; SVM, support vector machine; NN, neural network
Fig. 2
Fig. 2
Comparisons of clinical IVF outcomes among the three models using BMI, age, and clinical data (n = 72). a. F1 score comparisons. b. Accuracy comparisons. LR, logistic regression; SVM, support vector machine; NN, neural network
Fig. 3
Fig. 3
ROC curves for clinical pregnancy predictions (NN model)
See this image and copyright information in PMC

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