Application of Machine Learning Algorithms for Evaluating Predictors and Developing Diagnostic Models for Female Infertility Classification

Infertility affects millions worldwide, with estimates indicating that 1 in 6 people of reproductive age will experience it in their lifetime. Globally, infertility impacts between 12.6-17.5% of couples of reproductive age. Recently, machine learning (ML) has garnered significant attention in biomedical research, enabling creation of predictive models that can personalize disease treatment based on measurable variables, thereby aiding in the development of diagnostic tools. In this study, 28 predictor variables were selected preliminarily; after a multicollinearity test, 20 predictors were selected for the classification task and modelled as a binary supervised classification problem. Seven ML algorithms were evaluated, including Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, Naïve Bayes, K-Nearest Neighbour, and Extreme Gradient Boosting (XGBoost). Statistical analysis showed that anti-Müllerian hormone (AMH) can serve as a biomarker for diagnosing PCOS and evaluating ovarian reserve. Female fertility has been associated negatively with waist circumference (r = -0.35), systolic blood pressure (r = -0.30), poor ovarian reserve (r = -0.28), and triglycerides (r = -0.33), suggesting a possible link between these metabolic factors and female infertility. Among the models tested, Naïve Bayes and Logistic Regression provided the most reliable and generalizable performance. The incorporation of SHapley Additive exPlanations (SHAP) analysis enhanced the interpretability of the models, identifying polyendocrine metabolic ovarian syndrome (PMOS, previously known as polycystic ovarian syndrome-PCOS), AMH, poor ovarian reserve, menstrual cycle irregularity, systolic blood pressure, body mass index (BMI), fasting glucose, and triglycerides as the most influential predictors of female fertility. However, future studies incorporating data from multiple centres, comprising a larger, more representative population, and using more interpretable models could enhance the reliability of ML in clinical decision-making.