This exposure assessment measured personal RF electric field strength in multiple indoor and outdoor micro-environments in Malaysia using an ExpoM-RF 4 meter and modeled exposure with machine learning (FCNN, XG Boost) and linear regression. Reported exposures were usually below the stated public limit (61.4 V/m), but maximum values in dense urban areas with many base stations were reported to approach 56.7365 V/m. The authors frame near-threshold maxima in high-density areas as a potential health risk and recommend caution and monitoring.

This occupational epidemiology study used machine learning to evaluate whether workplace exposures (including magnetic and electric fields, vibration, noise, and heat stress) predict male reproductive indicators in 80 workers. The models and explainable AI outputs highlighted magnetic and electric field exposures and age as key predictors linked to lower free testosterone. The authors also report a 10-year forecast identifying electric field exposure as the most important long-term risk factor.

This analytical study evaluated machine learning models (SVM and Random Forest) to predict health symptoms in adults living near mobile phone base stations. The SVM model reportedly achieved high predictive performance for headache, sleep disturbance, dizziness, vertigo, and fatigue, and outperformed Random Forest and prior models. The abstract concludes that proximity to base stations is connected with increased prevalence of several symptoms and emphasizes distance, age, and duration of residence as key predictors.