ArticleJournal of assisted reproduction and genetics2020
Machine learning vs. classic statistics for the prediction of IVF outcomes.
Article in Journal of assisted reproduction and genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- Machine learning prediction of live birth after IVF using the morphological uterus sonographic assessment group features of adenomyosis.Scientific reports · 2026Article
- Development and External Validation of Machine Learning Model to Predict Live Birth Following Assisted Reproductive Technology in Women with Ovarian Endometriomas: A Decision-Support Tool.Clinical epidemiology · 2026Article
- Artificial Intelligence in Routine IVF Practice.Biology · 2025Review
- Interpretable machine learning model for predicting MII oocyte retrieved following controlled ovarian stimulation: a retrospective cohort study of 24,976 IVF/ICSI cycles.Journal of assisted reproduction and genetics · 2025Article
- Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.Journal of translational medicine · 2025Article
- Machine learning and microfluidic integration for oocyte quality prediction.Scientific reports · 2025Article
- Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.Scientific reports · 2025Article
- Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning.Journal of ovarian research · 2025Article
- Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.Scientific reports · 2025Article
- Assisting the Diagnosis of Cirrhosis in Chronic Hepatitis C Patients Based on Machine Learning Algorithms: A Novel Non-Invasive Approach.Journal of clinical laboratory analysis · 2025Observational
- Establishment and application of a preset dilution factor strategy for human chorionic gonadotropin testing in clinical laboratory.Frontiers in molecular biosciences · 2025Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer.Reproductive biology and endocrinology : RB&E · 2024Article
- Application of machine learning techniques in the diagnosis of endometriosis.BMC women's health · 2024Article
- Predicting risk of the subsequent early pregnancy loss in women with recurrent pregnancy loss based on preconception data.BMC women's health · 2024Article
- Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning.Human reproduction (Oxford, England) · 2023Article
- Construction of the machine learning-based live birth prediction models for the first in vitro fertilization pregnant women.BMC pregnancy and childbirth · 2023Article
- DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.
methodsThe 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.
resultsMachine 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.
conclusionsOur 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.