Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.

ABSTRACT: Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia (OA) or non-obstructive azoospermia (NOA). Reliable predictive models utilizing biomarkers could aid in clinical decision-making. This study included 352 azoospermia patients, with 152 diagnosed with OA and 200 with NOA. The data were randomly divided into a training set (244 cases) and a validation set (108 cases) for machine learning analysis. The training set was utilized for univariate and multivariate logistic regression to identify key predictors of NOA. Following this, nine machine learning. This study included 352 azoospermia patients, with 152 diagnosed with OA and 200 with NOA. The data were randomly divided into a training set (244 cases) and a validation set (108 cases) for machi

SUBMITTER: Xiao H 

PROVIDER: S-EPMC11828877 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature