Journal of Biomedical Physics and Engineering

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Staff Members

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

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

Document Type : Original Research

Authors

1 Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

2 Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

3 Department of Technical Physics, University of Eastern Finland, Kuopio, Finland

4 Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

10.31661/jbpe.v0i0.2310-1667

Abstract

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.

Highlights

Hossein Parsaei (Google Scholar)

Seyed Mahammad Javad Mortazavi (Google Scholar)

Keywords

References
  1. Calvente I, Pérez-Lobato R, Núñez MI, Ramos R, Guxens M, Villalba J, et al. Does exposure to environmental radiofrequency electromagnetic fields cause cognitive and behavioral effects in 10-year-old boys? Bioelectromagnetics. 2016;37(1):25-36. doi: 10.1002/bem.21951. PubMed PMID: 26769168.
  2. Woelders H, De Wit A, Lourens A, Stockhofe N, Engel B, Hulsegge I, et al. Study of potential health effects of electromagnetic fields of telephony and Wi-Fi, using chicken embryo development as animal model. Bioelectromagnetics. 2017;38(3):186-203. doi: 10.1002/bem.22026. PubMed PMID: 28092407.
  3. Mortazavi SAR, Mortazavi Gh, Mortazavi SMJ. Comments on Meo et al. Association of Exposure to Radio-Frequency Electromagnetic Field Radiation (RF-EMFR) Generated by Mobile Phone Base Stations with Glycated Hemoglobin (HbA1c) and Risk of Type 2 Diabetes Mellitus. Int. J. Environ. Res. Public Health, 2015, 12, 14519-14528. Int J Environ Res Public Health. 2016;13(3):261. doi: 10.3390/ijerph13030261. PubMed PMID: 26927149. PubMed PMCID: PMC4808924.
  4. Mortazavi SMJ, Mahbudi A, Atefi M, Bagheri Sh, Bahaedini N, Besharati A. An old issue and a new look: electromagnetic hypersensitivity caused by radiations emitted by GSM mobile phones. Technol Health Care. 2011;19(6):435-43. doi: 10.3233/THC-2011-0641. PubMed PMID: 22129944.
  5. Khurana VG, Hardell L, Everaert J, Bortkiewicz A, Carlberg M, Ahonen M. Epidemiological evidence for a health risk from mobile phone base stations. Int J Occup Environ Health. 2010;16(3):263-7. doi: 10.1179/107735210799160192. PubMed PMID: 20662418.
  6. Mortavazi S, Habib A, Ganj-Karami A, Samimi-Doost R, Pour-Abedi A, Babaie A. Alterations in TSH and Thyroid Hormones following Mobile Phone Use. Oman Med J. 2009;24(4):274-8. doi: 10.5001/omj.2009.56. PubMed PMID: 22216380. PubMed PMCID: PMC3243874.
  7. Abdel-Rassoul G, El-Fateh OA, Salem MA, Michael A, Farahat F, El-Batanouny M, Salem E. Neurobehavioral effects among inhabitants around mobile phone base stations. 2007;28(2):434-40. doi: 10.1016/j.neuro.2006.07.012. PubMed PMID: 16962663.
  8. Hutter HP, Moshammer H, Wallner P, Kundi M. Subjective symptoms, sleeping problems, and cognitive performance in subjects living near mobile phone base stations. Occup Environ Med. 2006;63(5):307-13. doi: 10.1136/oem.2005.020784. PubMed PMID: 16621850. PubMed PMCID: PMC2092490.
  9. Habash RW, Brodsky LM, Leiss W, Krewski D, Repacholi M. Health risks of electromagnetic fields. Part I: Evaluation and assessment of electric and magnetic fields. Crit Rev Biomed Eng. 2003;31(3):141-95. doi: 10.1615/critrevbiomedeng.v31.i3.10. PubMed PMID: 15018215.
  10. Habash RW, Brodsky LM, Leiss W, Krewski D, Repacholi M. Health risks of electromagnetic fields. Part II: Evaluation and assessment of radio frequency radiation. Crit Rev Biomed Eng. 2003;31(3):197-254. doi: 10.1615/critrevbiomedeng.v31.i3.20. PubMed PMID: 15018216.
  11. Santini R, Santini P, Le Ruz P, Danze JM, Seigne M. Survey study of people living in the vicinity of cellular phone base stations. Electromagnetic Biology and Medicine. 2003;22(1):41-9. doi: 10.1081/JBC-120020353.
  12. National Library of Medicine (US). Index Medicus. US Department of Health, Education, and Welfare, Public Health Service, National Institutes of Health, National Library of Medicine; 2002.
  13. Repacholi MH. Low-level exposure to radiofrequency electromagnetic fields: health effects and research needs. 1998;19(1):1-19. PubMed PMID: 9453702.
  14. Bortkiewicz A, Gadzicka E, Szyjkowska A, Politański P, Mamrot P, Szymczak W, Zmyślony M. Subjective complaints of people living near mobile phone base stations in Poland. Int J Occup Med Environ Health. 2012;25(1):31-40. doi: 10.2478/s13382-012-0007-9. PubMed PMID: 22219055.
  15. Eskander EF, Estefan SF, Abd-Rabou AA. How does long term exposure to base stations and mobile phones affect human hormone profiles? Clin Biochem. 2012;45(1-2):157-61. doi: 10.1016/j.clinbiochem.2011.11.006. PubMed PMID: 22138021.
  16. Alazawi SA. Mobile phone base stations health effects. Diyala J Med. 2011;1(1):44-52.
  17. Jooyan N, Mortazavi SMJ. Evidence Base on the Potential Carcinogenicity of Radiofrequency Radiation. JAMA Oncol. 2022;8(6):1. doi: 10.1001/jamaoncol.2022.0931. PubMed PMID: 35446369.
  18. Hansson Mild K, Mattsson MO, Jeschke P, Israel M, Ivanova M, Shalamanova T. Occupational Exposure to Electromagnetic Fields-Different from General Public Exposure and Laboratory Studies. Int J Environ Res Public Health. 2023;20(16):6552. doi: 10.3390/ijerph20166552. PubMed PMID: 37623138. PubMed PMCID: PMC10454245.
  19. Song X, Han M, Chen Y, Yue Y. Public exposure to broadband electromagnetic fields and its association with population density and building density: The case study of Beijing. Heliyon. 2023;9(6):e17153. doi: 10.1016/j.heliyon.2023.e17153. PubMed PMID: 37416650. PubMed PMCID: PMC10320032.
  20. Balmori A. Evidence for a health risk by RF on humans living around mobile phone base stations: From radiofrequency sickness to cancer. Environ Res. 2022;214(Pt 2):113851. doi: 10.1016/j.envres.2022.113851. PubMed PMID: 35843283.
  21. Hardell L, Koppel T. Electromagnetic hypersensitivity close to mobile phone base stations - a case study in Stockholm, Sweden. Rev Environ Health. 2022;38(2):219-28. doi: 10.1515/reveh-2021-0169. PubMed PMID: 35238501.
  22. McCredden JE, Cook N, Weller S, Leach V. Wireless technology is an environmental stressor requiring new understanding and approaches in health care. Front Public Health. 2022;10:986315. doi: 10.3389/fpubh.2022.986315. PubMed PMID: 36605238. PubMed PMCID: PMC9809975.
  23. Parsaei H, Faraz M, Mortazavi SMJ. A multilayer perceptron neural network–based model for predicting subjective health symptoms in people living in the vicinity of mobile phone base stations. 2017;9(2):99-105. doi: 10.1089/eco.2017.0011.
  24. Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics. 2018;15(1):41-51. doi: 10.21873/cgp.20063. PubMed PMID: 29275361. PubMed PMCID: PMC5822181.
  25. Taherisadr M, Dehzangi O, Parsaei H. Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis. Sensors (Basel). 2017;17(12):2895. doi: 10.3390/s17122895. PubMed PMID: 29236042. PubMed PMCID: PMC5750748.
  26. Shabaniyan T, Parsaei H, Aminsharifi A, Movahedi MM, Jahromi AT, Pouyesh S, Parvin H. An artificial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med. 2019;42(3):771-9. doi: 10.1007/s13246-019-00780-3. PubMed PMID: 31332724.
  27. Yang W, Wang K, Zuo W. Neighborhood component feature selection for high-dimensional data. J Comput. 2012;7(1):161-8. doi: 10.4304/jcp.7.1.161-168.
  28. Vapnik V. The nature of statistical learning theory. Springer Science & Business Media; 1999.
  29. Platt JC. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods - Support Vector Learning. MIT Press; 1999. p: 185-208.
  30. Parsaei H, Nezhad FJ, Stashuk DW, Hamilton-Wright A. Validation of motor unit potential trains using motor unit firing pattern information. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:974-7. doi: 10.1109/IEMBS.2009.5332849. PubMed PMID: 19963738.
  31. Blettner M, Schlehofer B, Breckenkamp J, Kowall B, Schmiedel S, Reis U, et al. Mobile phone base stations and adverse health effects: phase 1 of a population-based, cross-sectional study in Germany. Occup Environ Med. 2009;66(2):118-23. doi: 10.1136/oem.2007.037721. PubMed PMID: 19017702.
  32. Singh K, Nagaraj A, Yousuf A, Ganta S, Pareek S, Vishnani P. Effect of electromagnetic radiations from mobile phone base stations on general health and salivary function. J Int Soc Prev Community Dent. 2016;6(1):54-9. doi: 10.4103/2231-0762.175413. PubMed PMID: 27011934. PubMed PMCID: PMC4784065.
  33. Baliatsas C, Van Kamp I, Kelfkens G, Schipper M, Bolte J, Yzermans J, Lebret E. Non-specific physical symptoms in relation to actual and perceived proximity to mobile phone base stations and powerlines. BMC Public Health. 2011;11:421. doi: 10.1186/1471-2458-11-421. PubMed PMID: 21631930. PubMed PMCID: PMC3118249.
  34. Berg-Beckhoff G, Blettner M, Kowall B, Breckenkamp J, Schlehofer B, Schmiedel S, et al. Mobile phone base stations and adverse health effects: phase 2 of a cross-sectional study with measured radio frequency electromagnetic fields. Occup Environ Med. 2009;66(2):124-30. doi: 10.1136/oem.2008.039834. PubMed PMID: 19151228.
Journal of Biomedical Physics and Engineering
Volume 16, Issue 1 - Serial Number 1
75
February 2026
Pages 47-56
Files
Share
How to cite
Statistics