Version 1
: Received: 15 September 2023 / Approved: 18 September 2023 / Online: 19 September 2023 (04:33:05 CEST)
How to cite:
Parsaei, H.; Faraz, M.; Mortazavi, S.J. A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations. Preprints2023, 2023091193. https://doi.org/10.20944/preprints202309.1193.v1
Parsaei, H.; Faraz, M.; Mortazavi, S.J. A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations. Preprints 2023, 2023091193. https://doi.org/10.20944/preprints202309.1193.v1
Parsaei, H.; Faraz, M.; Mortazavi, S.J. A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations. Preprints2023, 2023091193. https://doi.org/10.20944/preprints202309.1193.v1
APA Style
Parsaei, H., Faraz, M., & Mortazavi, S.J. (2023). A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations. Preprints. https://doi.org/10.20944/preprints202309.1193.v1
Chicago/Turabian Style
Parsaei, H., Mehdi Faraz and SM Javad Mortazavi. 2023 "A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations" Preprints. https://doi.org/10.20944/preprints202309.1193.v1
Abstract
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.
Keywords
artificial intelligence; electromagnetic hypersensitivity (EHS); electromagnetic fields; machine learning; mobile phone base stations
Subject
Public Health and Healthcare, Public, Environmental and Occupational Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
