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

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 ۱۱ predictors related to participants’ living conditions. A total of ۶۹۹ 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 ۸۵.۳%, ۸۲%, ۸۴%, ۸۲.۴%, and ۶۵.۱% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were ۰.۹۹, ۰.۹۸, ۰.۹۲۰, ۰.۸۹, and ۰.۸۱. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of ۷۰.۰%, ۸۳.۴%, ۸۵.۳%, ۷۳.۰%, and ۶۹.۰% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (۷۰% vs. ۸% and ۱۱.۱% for SVM, Multilayer Perceptron Neural Network (MLPNN), and RF, respectively, and ۰.۸۱ vs. ۰.۶۲ and ۰.۶۴, 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.

کلیدواژه ها:

Artificial Intelligence ، Electromagnetic Hypersensitivity (EHS) ، Electromagnetic Fields ، Machine Learning ، Mobile Phone Base Stations