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 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.