ArticlePloS one2022
Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Construction and validation of a machine learning model for predicting early pregnancy in patients with polycystic ovary syndrome: a retrospective cohort study.Journal of ovarian research · 2026Article
- Review
- Prediction of in-vitro fertilization outcome by ultrasound strain analysis and machine learning: A multi-center study.The ultrasound journal · 2026Article
- Development and internal temporal validation of an explainable XGBoost model for predicting clinical pregnancy after frozen embryo transfer.Frontiers in endocrinology · 2026Article
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- The Influencing Factors and Predictive Algorithm of Pregnancy Outcomes in IVF/ICSI-ET Patients.International journal of women's health · 2026Article
- Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.Journal of translational medicine · 2025Article
- Machine learning in predicting infertility treatment success: A systematic literature review of techniques.Journal of education and health promotion · 2025Review
- 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
- The Effect of Hepatitis B Virus Infection on Semen Quality of Infertile Men: A Systematic Review and Meta-Analysis of Observational Studies.International journal of fertility & sterility · 2024Article
- 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
- Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.Seminars in reproductive medicine · 2024Review
- Exploring the potential of machine learning in gynecological care: a review.Archives of gynecology and obstetrics · 2024Review
- Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data.Animals : an open access journal from MDPI · 2024Article
- Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.PloS one · 2024Article
- A novel machine learning model for predicting clinical pregnancy after laparoscopic tubal anastomosis.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
- Conventional outcome reporting per IVF cycle/embryo transfer may systematically underestimate chances of success for women undergoing ART: relevant biases in registries, epidemiological studies, and guidelines.Human reproduction open · 2023Article
- Article
- The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAssisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF. MATERIALS AND
methodsA total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy outcomes at Taipei Medical University Hospital. Data used included patient characteristics and treatment. We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots.
resultsExperimental results showed that the random forest algorithm outperforms logistic regression in terms of areas under the receiver operating characteristics curve. The ovarian stimulation protocol is the most important factor affecting pregnancy outcomes. Long and ultra-long protocols have shown positive effects on clinical pregnancy among all protocols. Furthermore, total frozen and transferred embryos are positive for a clinical pregnancy, but female age and duration of infertility have negative effects on clinical pregnancy.
conclusionOur findings show the importance of variables and propensity of each variable by random forest algorithm for clinical pregnancy in the assisted reproductive technology cycle. This study provides a ranking of variables affecting clinical pregnancy and explores the effects of each treatment on successful pregnancy. Our study has the potential to help clinicians evaluate the success of IVF in patients.
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