Data-Sparse Prediction of High-Risk Schools for Lead Contamination in Drinking Water: Examples from Four U.S. States.

ABSTRACT: Childhood lead exposure through drinking water has long-term effects on cognition and development, and is a significant public health concern. The comprehensive lead testing of public schools entails high expense and time. In prior work, random forest modeling was used successfully to predict the likelihood of lead contamination in the drinking water from schools in the states of California and Massachusetts. In those studies, data from 70% of the schools was used to predict the probability of unsafe water lead levels (WLLs) in the remaining 30%. This study explores how the model predictions degrade, as the training dataset forms a progressively smaller proportion of schools. The size of the training set was varied from 80% to 10% of the total samples in four US states: California, Massach

SUBMITTER: Shrivatsa S 

PROVIDER: S-EPMC10572968 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature