Risk assessment of elevated blood lead concentrations in the adult population using a decision tree approach

Abstract

Lead is a common toxin which has detrimental effects on human health. Since lead poisoning is not associated with specific symptoms, diagnosing elevated blood lead concentration (EBLC) should be taken seriously. The purpose of this study was to propose a prediction model for EBLC based on demographic and clinical variables through a decision-tree model. In this cross-sectional study, 630 subjects (above 40 years old) living in South Khorasan Province, Iran in 2017 were selected via cluster random sampling method. From among the 630 participants who met the inclusion criteria, 70% (N = 456) were chosen randomly to achieve a set for developing the decision tree and multiple logistic regression (MLR). The other 30% (N = 174) were placed in a holdout sample to examine the function of the decision tree and MLR models. The predictive performance for various models was studied using the Receiver Operating Characteristic (ROC) curve. In the decision tree model, the parameters of hematocrit (HCT), White Blood Cell (WBC), Red Blood Cell (RBC), Mean corpuscular volume (MCV), creatinine concentration, abdominal pain, gender, route of administration, and history of cigarette smoking were the most critical factors in identifying people at risk of EBLC. The HCT concentration was the most critical variable, which was chosen as the root node of the tree. Based on the ROC curve, the decision tree model had better predictive accuracy than the logistic regression model. Our results indicated that the decision tree model offers far greater predictive precision than the logistic regression model. Doctors should pay more attention to some factors including the hematological parameters such as MCV, RBC, HCT, leukocytosis, creatinine levels, male sex, history of cigarette, and opium consumption for the screening of EBLCs.