Objective:

To develop a machine learning-based predictive model for identifying high sperm DNA fragmentation index (DFI) in infertile men using clinical and semen parameters.

Approach:

Key Findings:

• The RF model showed the best performance with a 10-fold cross-validation AUC of 0.979 (95% CI: 0.972−0.986) in the development cohort.
• In the external validation cohort, the RF model achieved an AUC of 0.945 (95% CI: 0.916−0.975).

Interpretation:

The RF model demonstrates good discrimination for predicting DFI abnormality in infertile men, but exhibits notable miscalibration in external validation, indicating systematic overestimation of risk.

Limitations:

• The model requires prospective validation and recalibration in independent populations before clinical application.
• The impact on patient-important reproductive outcomes remains unknown.

Conclusion:

The study provides a foundation for a predictive model of sperm DFI, but further research is needed to establish its clinical utility.

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