Occupational Exposure to Electromagnetic Fields—Different from General Public Exposure and Laboratory Studies

Hansson Mild, Kjell; Mattsson, Mats-Olof; Jeschke, Peter; Israel, Michel; Ivanova, Mihaela; Shalamanova, Tsvetelina

doi:10.3390/ijerph20166552

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…The exposure from broadcasting sites and base stations affects the entire body from a distance, while smartphones and smart gadgets only impact the head and hands in close proximity [18]. Recent studies show that mobile phone base stations are the primary source of the radiofrequency radiation spectrum [19].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

2024

J Biomed Phys Eng

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Background:The rapid increase in the number of Mobile Phone Base Stations (MPBS) has raised global concerns about the potential adverse health effects of exposure to Radiofrequency Electromagnetic Fields (RF-EMF). The application of machine learning techniques can enable healthcare professionals and policymakers to proactively address concerns surrounding RF-EMF exposure near MPBS.Objective: The current study aimed to investigate the potential of machine learning models for the prediction of health symptoms associated with RF-EMF exposure in individuals residing near MPBS. Material and Methods:This analytical study utilized Support Vector Machine (SVM) and Random Forest (RF) algorithms, incorporating 11 predictors related to participants' living conditions. A total of 699 adults participated in the study, and model performance was assessed using sensitivity, specificity, accuracy, and the Area Under Curve (AUC). Results:The SVM-based model demonstrated strong performance, with accuracies of 85.3%, 82%, 84%, 82.4%, and 65.1% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were 0.99, 0.98, 0.920, 0.89, and 0.81. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of 70.0%, 83.4%, 85.3%, 73.0%, and 69.0% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (70% vs. 8% and 11.1% for SVM, Multilayer Perceptron Neural Network (MLPNN), and RF, respectively, and 0.81 vs. 0.62 and 0.64, for SVM, MLPNN, and RF, respectively). Conclusion:Machine learning methods, specifically SVM, hold promise in effectively managing health symptoms in individuals residing near or planning to settle in the vicinity of MPBS.

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Section: Introductionmentioning

confidence: 99%

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

2024

J Biomed Phys Eng

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show abstract

“…The exposure from broadcasting sites and base stations affects the entire body from a distance, while smartphones and smart gadgets only affect the head and hands in close proximity [18]. Recent studies show that mobile phone base stations are the primary source of the radiofrequency radiation spectrum [19].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

Parsaei,

Faraz,

Mortazavi

2023

Preprint

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The rapid increase in the number of mobile phone base stations (MPBS) has raised global concerns about the potential adverse health effects of exposure to radiofrequency electromagnetic fields (RF-EMF). The application of machine learning techniques can enable healthcare professionals and policymakers to proactively address concerns surrounding RF-EMF exposure near MPBS. In this study, we investigated the potential of machine learning models to predict health symptoms associated with RF-EMF exposure in individuals residing near MPBS.We utilized support vector machine (SVM) and random forest (RF) algorithms, incorporating 11 predictors related to participants' living conditions. A total of 699 adults participated in the study, and model performance was assessed using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). The SVM-based model demonstrated strong performance, achieving accuracies of 85.3%, 82%, 84%, 82.4%, and 65.1% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were 0.99, 0.98, 0.920, 0.89, and 0.81. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of 70.0%, 83.4%, 85.3%, 73.0%, and 69.0% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (70% vs 8% and 11.1% for SVM, MLPNN, and RF, respectively, and 0.81 vs 0.62 and 0.64, for SVM, MLPNN, and RF, respectively). These findings suggest that machine learning methods, specifically SVM, hold promise in effectively managing health symptoms in individuals residing near or planning to settle in the vicinity of MPBS.

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A scoping review on advantages and drawbacks of nanotechnology in the field of occupational health

Feyzi,

Alboghobeish,

Esmaeili

et al. 2024

Nanotechnol. Environ. Eng.

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Occupational Exposure to Electromagnetic Fields—Different from General Public Exposure and Laboratory Studies

Cited by 8 publications

References 47 publications

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations

A scoping review on advantages and drawbacks of nanotechnology in the field of occupational health

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