Predictive Models of Pregnancy Based on Male Partner Data From a Preconception Cohort Study

BACKGROUND: Male factors contribute to approximately 50% of couple infertility, yet few studies have used machine learning with comprehensive male partner lifestyle and behavioral data to predict spontaneous conception. OBJECTIVES: To develop and validate machine learning models predicting spontaneous conception using detailed self-reported male partner characteristics from a North American prospective preconception cohort. MATERIALS AND METHODS: We analyzed 2843 couples from the Pregnancy Study Online (PRESTO; 2013-2024). Using 185 male partner variables spanning sociodemographic, lifestyle, dietary, anthropometric, medical, and reproductive history domains (with female age and BMI included as covariates), we developed models for: (1) per-cycle probability of conception (Model I) using a discrete-time proportional hazards model; (2) pregnancy in fewer than 12 cycles (Model II); and (3) pregnancy within 6 cycles (Model III) using regularized logistic regression, gradient boosted trees, and random forests. We evaluated model performance using fivefold cross-validation. Age-stratified analyses (≥30 vs. <30 years) assessed heterogeneity in predictive factors. RESULTS: Among 2843 couples, 87.3% reported pregnancy in fewer than 12 cycles and 69.7% within 6 cycles. Model I achieved a concordance index of ∼0.59 (SD 0.012). The sparse regularized logistic regression model, selected via recursive feature elimination for interpretability, achieved ROC AUCs of 0.653 (SD 0.027) for Model II and 0.639 (SD 0.016) for Model III-comparable to the best full models using all candidate predictors (AUC 0.665 and 0.648, respectively). Prior impregnation history was the strongest predictor (Model II OR: 1.48, 95% CI: 1.34-1.63; Model III OR: 1.34, 95% CI: 1.21-1.47). Additional predictors included sugar-sweetened beverage consumption, environmental tobacco smoke exposure, gastroesophageal reflux disease, and physical activity. Male age emerged as a significant predictor among males ≥30 years, whereas female BMI, cycling ≥ 3 h/week, and tobacco smoke exposure were key predictors among younger males. DISCUSSION AND CONCLUSIONS: Machine learning models incorporating male partner characteristics demonstrated moderate discrimination for predicting pregnancy within 6-12 cycles. Predictive factors differed by male age, highlighting heterogeneity that may warrant further investigation. Modifiable factors, particularly sugar-sweetened beverage consumption, tobacco smoke exposure, and physical activity, were among the predictors identified and may represent candidate targets for future intervention research.