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A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations | ||
| Journal of Biomedical Physics and Engineering | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 27 دی 1402 اصل مقاله (783.25 K) | ||
| نوع مقاله: Original Research | ||
| شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2310-1667 | ||
| نویسندگان | ||
| Hossein Parsaei1، 2؛ Mehdi Faraz3؛ Seyed Mohammad Javad Mortazavi* 1، 4 | ||
| 1Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran | ||
| 2Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran | ||
| 3Department of Technical Physics, University of Eastern Finland, Kuopio, Finland | ||
| 4Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Artificial Intelligence؛ Electromagnetic Hypersensitivity (EHS)؛ Electromagnetic Fields؛ Machine Learning؛ Mobile Phone Base Stations | ||
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آمار تعداد مشاهده مقاله: 154,090 تعداد دریافت فایل اصل مقاله: 144 |
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