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

Authors: Parsaei H, Faraz M, Mortazavi SMJ

Year: 2024

Category: Epidemiology, Public Health, Machine Learning

Journal: Journal of Biomedical Physics and Engineering

DOI: 10.31661/jbpe.v0i0.2310-1667

URL: https://jbpe.sums.ac.ir/article_49803.html

Abstract

Overview

The rapid increase in the number of Mobile Phone Base Stations (MPBS) worldwide has given rise to significant global concerns about potential adverse health effects stemming from exposure to Radiofrequency Electromagnetic Fields (RF-EMF). This study investigates the use of machine learning models as a proactive means for healthcare professionals and policymakers to address and manage concerns related to RF-EMF exposure—especially among individuals living near MPBS.

Objective

This research aimed to explore whether machine learning models could accurately predict health symptoms that are associated with living near mobile phone base stations, thereby enabling more effective management of these risks.

Material and Methods

  • Analytical study using Support Vector Machine (SVM) and Random Forest (RF) algorithms.
  • Included 11 predictors relevant to participants' living conditions.
  • Total of 699 adult participants.
  • Model performance measured by sensitivity, specificity, accuracy, and Area Under Curve (AUC).

Findings

  • SVM-based model accuracies for:
    • Headache: 85.3%
    • Sleep disturbance: 82%
    • Dizziness: 84%
    • Vertigo: 82.4%
    • Fatigue: 65.1%
  • Corresponding AUC values: 0.99, 0.98, 0.92, 0.89, and 0.81, respectively.
  • SVM model consistently outperformed RF and previously developed models, especially for fatigue sensitivity (70% SVM vs. 8% MLPNN vs. 11.1% RF).

Key predictors identified were: distance from the base station, age, and duration of residence.

Conclusion

Machine learning methods—most notably SVM—show considerable promise for the management and prediction of health symptoms in individuals living near or considering residing near mobile phone base stations. There is a connection between proximity to mobile base stations and increased prevalence of headaches, sleep disturbances, dizziness, irritability, concentration issues, and neuropsychiatric risks, reaffirming concerns about health risks from electromagnetic field exposure.

Health Effects Linked to EMF Exposure

  • Studies cited support that people living closer to base stations (<500 m) experience more health complaints.
  • Symptoms found include: sleep disturbance, headaches, dizziness, irritability, concentration difficulties, hypertension, neuropsychiatric problems (memory changes, tremors, depression, etc.)
  • These findings strengthen the link between RF-EMF exposure from MPBS and a spectrum of negative health outcomes.
radiofrequency electromagnetic fields mobile phone base stations health symptoms machine learning SVM public health RF-EMF exposure
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