Materials and Methods
Peripheral venous blood was drawn from each child into two separate blood collection tubes for the measurement of hematology and blood lead levels. The Sysmex XE5000 hematology analyzer (Sysmex, Kobe, Japan) was used to automatically measure hematology parameters from whole blood. Additionally, the lead level in whole blood was semi-automatically determined by the Bohui BH2101s atomic absorption spectrometer (Bohui, Beijing, China). Briefly, the atomic absorption spectrometer was preheated for commissioning. The instrument’s software automatically set the parameters to optimal conditions. A standard curve ( r ≥ 0.9950) was then constructed based on the absorbance values of the corresponding calibration solution. Finally, we added 40 µl of whole blood to 0.36 ml of diluent, mixed them well, and measured the blood lead concentration after half an hour at room temperature.

In our study dataset, we included both hematology indicators with significant differences identified in this research and biochemical indicators with significant differences from another study previously published by our authors ( Ye et al., 2021 ), both derived from the same cohort of participants, as continuous attributes. Additionally, types of lead exposure and neurological symptoms were included as categorical attributes. Neurological symptoms were used as the outcome variable, while all others served as independent variables. During data processing, we performed integer encoding on categorical variables for subsequent analysis. The dataset was randomly split into a training set (80%) and a validation set (20%). We selected three models for prediction: Random Forest, support vector machine (SVM), and generalized linear model (GLM), all trained using the full range of available features. The outcome variable for these models was the status of neurological damage in children with lead poisoning, classified based on clinical assessments and diagnostic results. Model performance was evaluated on the validation set by calculating the confusion matrix and receiver operating characteristic (ROC) curve, to select the best predictive model. We further utilized the recursive feature elimination (RFE) method in conjunction with the best predictive model to identify the most critical features for predicting neurological damage in lead-poisoned children. The final selection of features was based on their importance scores in the model. All analyses and model building were conducted in the R environment, using packages such as scikit-learn, randomForest, and e1071. We provided a detailed summary and coefficient analysis of the final logistic regression model, along with an ROC curve to demonstrate its performance. The best cut-off point for the indicator with optimal sensitivity and specificity was identified, followed by risk stratification and subgroup analyses.

Data analysis was conducted using RStudio 4.3.0 ( RStudio Team, 2023 ) and MedCalc 11.4.2 ( https://www.medcalc.org/ ). Distribution normality was assessed using the Kolmogorov–Smirnov test, with results presented as mean ± SD for normally distributed data and median (inter-quartile range) for skewed data. Differences between the LP and HC groups were evaluated using independent Student t-tests for normally distributed variables and the Mann–Whitney U-test for non-parametrically distributed variables. Categorical variable differences were analyzed using the χ 2 test. Statistically significant indicators were further analyzed with machine learning techniques. Spearman correlation analysis was employed to evaluate the independence of these indicators. Statistical significance was set at a P -value of less than 0.05.

The Medical Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University, Yuying Children’s Hospital of Wenzhou Medical University granted Ethical approval to carry out the study within its facilities (Ethical Application Ref: 2022-K-106-02).