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Article

Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children

1
CNR IEIIT—Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, 20133 Milan, Italy
2
Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, 20133 Milan, Italy
3
EDF Electricite de France, 92300 Levallois-Perret, France
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(7), 1230; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16071230
Received: 20 February 2019 / Revised: 29 March 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
Characterization of children exposure to extremely low frequency (ELF) magnetic fields is an important issue because of the possible correlation of leukemia onset with ELF exposure. Cluster analysis—a Machine Learning approach—was applied on personal exposure measurements from 977 children in France to characterize real-life ELF exposure scenarios. Electric networks near the child’s home or school were considered as environmental factors characterizing the exposure scenarios. The following clusters were identified: children with the highest exposure living 120–200 m from 225 kV/400 kV overhead lines; children with mid-to-high exposure living 70–100 m from 63 kV/150 kV overhead lines; children with mid-to-low exposure living 40 m from 400 V/20 kV substations and underground networks; children with the lowest exposure and the lowest number of electric networks in the vicinity. 63–225 kV underground networks within 20 m and 400 V/20 kV overhead lines within 40 m played a marginal role in differentiating exposure clusters. Cluster analysis is a viable approach to discovering variables best characterizing the exposure scenarios and thus it might be potentially useful to better tailor epidemiological studies. The present study did not assess the impact of indoor sources of exposure, which should be addressed in a further study. View Full-Text
Keywords: children; ELF MF; magnetic field; indoor exposure; cluster analysis; Machine Learning children; ELF MF; magnetic field; indoor exposure; cluster analysis; Machine Learning
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MDPI and ACS Style

Tognola, G.; Bonato, M.; Chiaramello, E.; Fiocchi, S.; Magne, I.; Souques, M.; Parazzini, M.; Ravazzani, P. Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children. Int. J. Environ. Res. Public Health 2019, 16, 1230. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16071230

AMA Style

Tognola G, Bonato M, Chiaramello E, Fiocchi S, Magne I, Souques M, Parazzini M, Ravazzani P. Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children. International Journal of Environmental Research and Public Health. 2019; 16(7):1230. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16071230

Chicago/Turabian Style

Tognola, Gabriella, Marta Bonato, Emma Chiaramello, Serena Fiocchi, Isabelle Magne, Martine Souques, Marta Parazzini, and Paolo Ravazzani. 2019. "Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children" International Journal of Environmental Research and Public Health 16, no. 7: 1230. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16071230

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