Analyzing the Impact of Occupational Exposures on Male Fertility Indicators: A Machine Learning Approach

Authors: Mohammadi H, Khoddam S, Golbabaei F, Dehghan SF

Year: 2025 May 29

Category: Epidemiology

Journal: Reproductive Toxicology

DOI: 10.1016/j.reprotox.2025.108959

URL: https://pubmed.ncbi.nlm.nih.gov/40449633/

Abstract

Overview

Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using advanced machine learning models. The primary aim is to identify key risk factors and provide predictive insights into workers' reproductive health over the next decade.

  • Data were collected from 80 male workers in an automobile part manufacturing plant.
  • Demographic characteristics, occupational exposures, biochemical markers, hormone levels, and sperm parameters were analyzed.

Findings

  • Five machine learning models (logistic regression, bagging classifier, extreme gradient boosting, random forest, and support vector machine) were trained and evaluated using 5-fold cross-validation to determine effective predictors of reproductive health outcomes.
  • Exposure to whole-body vibration, magnetic fields, electric fields, and heat stress were found to closely affect free testosterone levels.
  • SHAP analysis indicated Magnetic Field Exposure (0.339) and Wet Bulb Globe Temperature (0.138) as significant contributors, with Worker Age (0.244) being the most influential demographic factor negatively impacting Free Testosterone.
  • The XGBoost and Random Forest models achieved the highest predictive accuracy with AUC (0.99).
  • Random Forest model Importance: Electric Field Exposure (5%) and Magnetic Field Exposure (4.7%) showed the most substantial negative impact on Free Testosterone, followed by Worker Age (4.1%).

Conclusion

This study concludes that machine learning, particularly tree-based models like Random Forest and XGBoost, can effectively identify key occupational and demographic factors influencing male reproductive health. Electric and magnetic field exposures, age, work experience, and oxidative stress biomarkers emerged as the most critical predictors. Explainable AI methods revealed complex interactions among these factors. The 10-year forecast highlighted electric field exposure as the most significant long-term risk. These findings emphasize the need for targeted interventions to reduce electromagnetic and vibration exposures and to protect aging workers.

⚠️ The study highlights a clear connection between electromagnetic field exposure and decreased male reproductive health indicators, noting significant negative impacts from both magnetic and electric fields.

occupational exposure male fertility machine learning electromagnetic fields vibration reproductive health workplace safety
← Back to Stats