A machine learning approach using semen parameters and sperm mitochondrial DNA copy number to predict couples' fecundity.

ABSTRACT:

Objective

To examine the utility of semen parameters and sperm mitochondrial DNA copy number (mtDNAcn) to predict couples' time to pregnancy (TTP).

Design

This study assessed the predictive power of sperm mtDNAcn and 34 semen parameters. Two composite semen quality indices (SQIs) were developed; an unweighted ranked-sperm quality index (ranked-SQI) derived from only semen parameters and a weighted sperm quality index generated using machine learning via elastic net (ElNet-SQI). Discrete-time proportional hazard models, logistic regression, and receiver operating characteristic (ROC) analyses were used to evaluate the predictive ability of achieving pregnancy at 3, 6, and 12 months, and the overall TTP.

Subjects

The participants included 281 men from the Longitudinal I

SUBMITTER: Sawant S 

PROVIDER: S-EPMC12496432 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature