C, The XGBoost algorithm utilized to build the classification model and calculate the S-index. D, Methods used to interpret the model.

XGBoost had a higher accuracy (area under the receiver operator characteristic curve [AUC] = 0.747) than the 2 other classifiers, logistic regression (AUC = 0.701) and random forest (AUC = 0.719).

This diagram outlines the process used to identify the top 20 metabolites associated with oxidative potential (OP) in heavy metal-contaminated soils. The analysis involves data collection from ...

Using the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited ...

The algorithm was trained using XGBoost 1.4.1.1 R library programmed with R v3.6.3. Results: The lung cancer cohort was heavily weighted towards early-stage lung cancer (87.7% stage I/II), including ...