Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study.

Journal: BMC urology
PMID: 39075422

Abstract

BACKGROUND: The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies.

Authors

  • Shun-Shun Cao
    Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
  • Xiao-Ming Liu
    Department of Geological Sciences, University of North Carolina, Chapel Hill, N.C., United States of America.
  • Bo-Tian Song
    Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
  • Yang-Yang Hu
    Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China. 209204@wzhealth.com.

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