Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children.
Int J Environ Res Public Health
; 16(7)2019 04 06.
Article
em En
| MEDLINE
| ID: mdl-30959870
ABSTRACT
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 120â»200 m from 225 kV/400 kV overhead lines; children with mid-to-high exposure living 70â»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. 63â»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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Exposição Ambiental
/
Campos Magnéticos
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Limite:
Adolescent
/
Child
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Child, preschool
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Humans
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Infant
/
Newborn
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article