Machine learning-based prediction of treatment outcomes and quantitative analysis of contributing factors in fertility induction therapy for adolescent male patients with congenital hypogonadotropic hypogonadism.

Objective: Congenital Hypogonadotropic Hypogonadism (CHH) is a rare disease with an extremely low incidence, and the outcomes of fertility induction therapy in CHH patients exhibit significant interindividual heterogeneity. Given the context of scarce samples and heterogeneous phenotypes, conventional statistical methods struggle to integrate multidimensional data and quantify the contribution of influencing factors. In contrast, machine learning (ML) techniques offer unique advantages in integrating high-dimensional complex medical data and uncovering hidden relationships. To date, the application of ML for predicting treatment outcomes in CHH remains unexplored. Therefore, this study aims to, for the first time, utilize an ML algorithm to construct and validate a predictive model based on a limited clinical cohort and thereby provide a basis for the individualized treatment of CHH. Methods: In this single-center retrospective cohort study, 65 adolescent male CHH patients undergoing fertility induction therapy were enrolled and categorized into success (nocturnal emission, n = 55) and failure (non-ejaculation, n = 10) groups based on treatment outcomes. Fifteen pre-treatment baseline indicators across four categories were collected. A random forest model was constructed, employing the Synthetic Minority Over-sampling Technique (SMOTE) and 5-fold stratified cross-validation to mitigate class imbalance and overfitting. Key predictors were identified via feature importance ranking, and decision thresholds were optimized using ROC curves. The model's performance was comprehensively evaluated and compared against other ML methods. Results: The random forest model demonstrated excellent and stable predictive performance: Accuracy 0.84 ± 0.12, Precision 0.82 ± 0.13, Recall 0.89 ± 0.08, F1 score 0.85 ± 0.10, and AUC 0.95 ± 0.04. Feature importance analysis identified the top five predictors: cryptorchidism (the strongest predictor), pre-treatment penile diameter, penile length, follicle-stimulating hormone (FSH) level, and anti-Müllerian hormone (AMH) level. Comparative analysis confirmed the superior comprehensive performance of the random forest model. Conclusion: This study successfully developed a robust machine learning model for predicting CHH treatment outcomes. It not only validates the methodological utility of ML in small-sample rare disease research but also elucidates the physiological basis of treatment response through interpretable feature clusters. The model provides clinicians with a quantifiable tool for risk stratification and paves the way for personalized therapeutic decision-making in CHH.