Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003–2018

score of 0.145, a G-mean of 0.749, and a Youden index of 0.498 in the test set. Four interpretable methods were integrated into the ML model. RF found that specific levels of blood lead (Pb) (0.449-29.964 µg/dL), urinary cesium (Cs) (1.822-270.426 µg/L), and urinary antimony (Sb) (0.015-4.953 µg/L) were positively associated with the PCA risk, while blood cadmium (Cd) (0.247-9.025 µg/L) showed a negative association. Notably, urinary Cs and Sb emerged as novel risk-related metals for the PCA in our study. The synergistic effect analysis further identified blood Pb, urinary Sb, and urinary Cs as the major contributing factors. The predictive model established in this study can provide valuable strategies for the prevention and the control of PCA.