Table 2 Prediction performance of internal validation in CMUH

CatBoost

0.755 (0.743–0.767)

48.70% (47.52–49.89)

83.12% (81.43–84.72)

90.64% (89.77–91.45)

32.56% (31.91–33.23)

XGBoost

0.749 (0.736–0.761)

51.50% (50.31–52.69)

81.08% (79.32–82.75)

90.13% (89.28–90.92)

33.25% (32.55–33.97)

Random Forest

0.733 (0.720–0.745)

33.80% (34.67–36.95)

88.18% (86.71–89.55)

91.04% (90.00–91.99)

29.05% (28.56–29.54)

Decision tree

0.704 (0.691–0.717)

44.05% (42.87–45.23)

81.76% (80.02–83.41)

89.02% (88.05–89.91)

30.34% (29.72–30.96)

Logistic regression

0.694 (0.681–0.707)

28.71% (27.65–29.80)

89.80% (88.46–91.03)

89.80% (88.55–90.93)

27.13% (28.30–29.14)