Background This study explores the utility of machine learning (ML) models in predicting complicated Ovarian Hyperstimulation Syndrome (OHSS) in patients undergoing infertility treatments, addressing the challenge posed by highly imbalanced datasets. Objective This research fills the existing void by introducing a detailed structure for crafting diverse machine learning models and enhancing data augmentation methods to predict complicated OHSS effectively. Importantly, the research also concentrates on pinpointing critical elements that affect OHSS. Method This retrospective study employed a ML framework to predict complicated OHSS in patients undergoing infertility treatment. The dataset included various patient characteristics, treatment details, ovarian response variables, oocyte quality indicators, embryonic development metrics, sperm quality assessments, and treatment specifics. The target variable was OHSS, categorized as painless, mild, moderate, or severe. The ML framework incorporated Ray Tune for hyperparameter tuning and SMOTE-variants for addressing data imbalance. Multiple ML models were applied, including Decision Trees, Logistic Regression, SVM, XGBoost, LightGBM, Ridge Regression, KNN, and SGD. The models were integrated into a voting classifier, and the optimization process was conducted. The SHAP package was used to interpret model outcomes and feature contributions. Results The best model incorporated IPADE-ID augmentation along with an ensemble of classifiers (SGDClassifier, SVC, RidgeClassifier), reaching a recall of 0.9 for predicting OHSS occurrence and an accuracy of 0.76. SHAP analysis identified key factors: GnRH antagonist use, longer stimulation, female infertility factors, irregular menses, higher weight, hCG triggers, and, notably, higher number of embryos . Conclusion This novel study demonstrates ML's potential for predicting complicated OHSS. The optimized model provides insights into contributory factors, challenging certain conventional assumptions. The findings highlight the importance of considering patient-specific factors and treatment details in OHSS risk assessment.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This retrospective study analyzed anonymized medical records with approval from the Mashhad University of Medical Sciences ethics committee (IR.MUMS.REC.1395.326). Data were accessed on July 11, 2023, and contained no identifying variables. Verbal consent was obtained from all patients for the use of their anonymized data, in accordance with ethical guidelines and data protection regulations.

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The dataset supporting the conclusions of this article is confidential and cannot be made publicly available. However, data may be available from the corresponding author upon reasonable request and with permission of the original data providers, subject to compliance with applicable confidentiality agreements and data protection laws.