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Health·G Space·Evidence-backed gain·Published 2026-08-16

Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model

Abstract: Background 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. Aim 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. Methods Data from 4116 men in the NHANES database (2001-2004) formed the training/internal validation sets. An independ…

TRV-2026-0778Peer-reviewedPermanent record — cite & verify
Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model

378th EMDS hosts Walking Blood Bank Pre-screening (9006793) by U.S. Air Force photo by Senior Airman Kevin Dunkleberger. Public domain

The quick read

Researchers developed and validated a machine learning model to predict erectile dysfunction risk from routine blood test data, using 4116 NHANES participants for training and internal validation and 489 NPTR-confirmed patients for independent external validation. The random forest model achieved the best results in external validation.

The work matters because ED is described as a prevalent disorder and sentinel marker for cardiovascular disease, yet current diagnosis relies on subjective questionnaires or invasive exams. A blood-based screening tool could shift detection earlier in primary care, but real-world adoption, generalizability beyond the studied cohorts, and clinical workflow integration remain unproven.

Main points
  • Training data included 4116 men from NHANES 2001-2004 with internal validation, plus 489 clinical patients with NPTR-confirmed ED for external validation.
  • From 49 initial indicators, nine key predictors were selected via univariate logistic, multivariate logistic, and LASSO regression.
  • Seven models were optimized via grid search with fivefold cross-validation and evaluated with ROC analysis, calibration curves, and decision curve analysis.
  • SHAP analysis identified age, sex hormone-binding globulin (SHBG), testosterone, glucose, cholesterol, and creatinine as most influential predictors.
Gain

A random forest model using nine routine blood-based predictors can screen for erectile dysfunction risk with high external validation performance, enabling early non-invasive detection during health check-ups.

The rundown

The study started with 49 demographic and blood-based indicators and narrowed to nine predictors using three selection methods. Seven machine learning models were built and tuned with grid search and fivefold cross-validation, then compared using ROC, calibration, and decision curve analysis.

The random forest model outperformed logistic regression, which had AUC = 0.743, and was interpreted via SHAP to rank predictor importance. Authors frame the nine-feature panel as suitable for routine health examinations to support precision management.

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