ArticleFrontiers in endocrinology2023
Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 21 citations in OpenAlex.
- 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
- Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.European radiology · 2026Article
- The Influencing Factors and Predictive Algorithm of Pregnancy Outcomes in IVF/ICSI-ET Patients.International journal of women's health · 2026Article
- A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment.Frontiers in medical technology · 2026Article
- Application of deep learning-clinical baseline feature fusion model to predict postoperative mortality in elderly patients with hip fracture: a multicenter study.Frontiers in medicine · 2026Article
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- Clinical Characteristics of Patients With Respiratory Infections After Nonpharmacological Interventions for COVID-19 in China Have Ended: Using Machine Learning Approaches to Support Pathogen Prediction at Admission.Immunity, inflammation and disease · 2025Article
- The Influence of Pre-IVF Day 2 TSH Levels on Treatment Success and Obstetric Outcomes: A Retrospective Single-Center Analysis with Machine Learning-Based Data Evaluation.Journal of clinical medicine · 2025Article
- Predicting IVF outcomes using a logistic regression-ABC hybrid model: A proof-of-concept study on supplement associations.PloS one · 2025Article
- Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.PloS one · 2025Article
- Advanced KPI framework for IVF pregnancy prediction models in IVF protocols.Scientific reports · 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
- Machine learning-based sales forecasting during crises: Evidence from a Turkish women's clothing retailer.Science progressArticle
- A machine learning model for predicting fertilization following short-term insemination using embryo images.Reproductive medicine and biologyArticle
- Development and Validation of a Combined Model Integrating Early Pregnancy Ultrasound Radiomics and Clinical Features to Predict Live Birth Following Single Vitrified-Warmed Blastocyst Transfer.Reproductive medicine and biologyArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following Methods: This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times. Results: Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features. Conclusion: This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.
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