Use of machine learning in the analysis of indoor ELF MF exposure in children

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 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.