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Table 3 The predictive performance of models for Grade 3 xerostomia

From: A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy

  Sensitivity (95%CI) Specificity (95%CI) PPV (95%CI) NPV (95%CI) AUC (95%CI) Accuracy (95%CI)
Training set       
RF 0.955 (0.868–1.000) 0.961 (0.937–0.986) 0.700 (0.536–0.864) 0.996 (0.987–1.000) 0.986 (0.972–1.000) 0.961 (0.937–0.985)
XGB 0.864 (0.720–1.000) 0.858 (0.814–0.903) 0.365 (0.235–0.496) 0.985 (0.969–1.000) 0.914 (0.844–0.984) 0.859 (0.816–0.902)
DTC 0.500 (0.291–0.709) 0.991 (0.980–1.000) 0.846 (0.650–1.000) 0.955 (0.928–0.981) 0.746 (0.639–0.853) 0.949 (0.922–0.976)
Testing set       
RF 0.333 (0.025–0.641) 0.851 (0.782–0.921) 0.167 (0.000–0.339) 0.935 (0.884–0.985) 0.766 (0.626–0.905) 0.809 (0.736–0.883)
XGB 0.444 (0.120–0.769) 0.792 (0.713–0.871) 0.160 (0.016–0.304) 0.941 (0.891–0.991) 0.661 (0.478–0.843) 0.764 (0.684–0.843)
DTC 0.222 (0.000–0.494) 0.980 (0.953–1.000) 0.500 (0.010–0.990) 0.934 (0.887–0.981) 0.601 (0.457–0.746) 0.918 (0.867–0.969)
  1. RF random forest, DTC decision tree classifier, XGB extreme-gradient boosting, PPV positive predictive value, NPV negative predictive value, AUC area under the curve