Prediction of chemical miscarriage in assisted reproductive technology pregnancies using machine learning: A developmental study

Background: Early miscarriage is a common complication following assisted reproductive technology (ART), affecting ۲۰-۳۰% of conceptions and creating challenges for patient counseling and treatment planning. Accurate prediction remains difficult because of the multifactorial nature of miscarriage and variability in ART outcomes. Machine learning (ML) offers promising opportunities for individualized risk assessment. Objective: This study aimed to develop an ML-based framework for early prediction of chemical miscarriage in infertile women undergoing ART using only pre-treatment baseline clinical data. Materials and Methods: In this developmental study, clinical data from ۳۰۰۰ infertile couples who attended the Yazd Reproductive Sciences Institute, Yazd, Iran, between March ۲۰۲۰ and September ۲۰۲۲ were extracted from medical records. After preprocessing, including missing data handling, outlier correction, normalization, and class balancing, a benchmark dataset of ۱۲۳۴ samples with ۳۲ features were constructed and made publicly available. Multiple ML algorithms and feature selection methods were evaluated. Results: Across both imbalanced and balanced datasets using synthetic minority oversampling technique and adaptive synthetic sampling, the random forest model achieved the best performance, with area under the receiver operating characteristic curve values of ۷۰.۹%, ۷۰.۳%, and ۷۰.۸%, and corresponding accuracies of ۸۲.۲%, ۸۱.۱%, and ۸۰.۰%, respectively. ۱۲ clinically relevant predictors were identified, including maternal age, body mass index, previous abortions, thyroid-stimulating hormone levels, sperm parameters, ART-related protocols, history of intrauterine insemination, and menstrual regularity. Conclusion: ML models, particularly random forest, can provide meaningful early prediction of miscarriage risk using baseline clinical data. The publicly available benchmark dataset supports reproducible research and future advances in predictive reproductive medicine.

کلیدواژه ها:

Infertility ، Pregnancy loss ، Assisted reproductive technology ، Machine learning ، Prediction ، Classification. ، ناباروری ، سقط جنین ، فناوری کمک باروری ، یادگیری ماشین ، پیش بینی ، طبقه بندی.