Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine...

Erectile dysfunction (ED) is a prevalent male health disorder and a recognized sentinel marker for cardiovascular disease. Current diagnostic reliance on subjective questionnaires or invasive examinations limits early screening.

We aimed to develop and validate a machine learning model based on routine blood test data to predict ED risk to facilitate its early clinical screening.

Data from 4116 men in the NHANES database (2001-2004) formed the training/internal validation sets. An independent external validation set comprised 489 clinical patients with NPTR-confirmed ED. From 49 initial demographic and blood-based indicators, feature selection via univariate logistic, multivariate logistic, and LASSO regression identified nine key predictors. Seven machine learning models were constructed, optimized via grid search with fivefold cross-validation, and evaluated using ROC analysis, calibration curves, and decision curve analysis (DCA). The optimal model was interpreted via SHAP.

The random forest model achieved superior performance, with an external validation AUC of 0.934, accuracy of 0.918, and specificity of 0.986, significantly outperforming logistic regression (AUC = 0.743). SHAP analysis identified age, sex hormone-binding globulin (SHBG), testosterone, glucose, cholesterol, and creatinine as the most influential predictors.

The study established a concise, nine-feature blood test panel and validated a high-performing predictive model in an independent clinical cohort, demonstrating significant clinical net benefit. Meanwhile, this model provides an economical, non-invasive, and scalable screening tool suitable for health check-ups, facilitating early ED identification.

A machine learning model based on routine blood tests can effectively evaluate ED risk, offering a novel foundation for early screening and precision management.