Table 2 Construction and performance validations of machine learning models

In training set

GBDT

0.994 (0.988-1.000)

Ref

0.994 (0.988-1.000)

0.996 (0.989-1.000)

0.992 (0.982-1.000)

0.992 (0.982-1.000)

0.996 (0.989-1.000)

LR

0.890 (0.860–0.920)

< 0.001

0.834 (0.802–0.866)

0.810 (0.762–0.857)

0.858 (0.815-0.900)

0.852 (0.808–0.896)

0.817 (0.771–0.863)

AdaBoost

0.918 (0.894–0.941)

< 0.001

0.918 (0.894–0.941)

0.962 (0.939–0.985)

0.873 (0.833–0.914)

0.885 (0.848–0.922)

0.958 (0.932–0.983)

SVM

0.912 (0.888–0.936)

< 0.001

0.912 (0.888–0.936)

0.924 (0.892–0.956)

0.900 (0.864–0.936)

0.903 (0.868–0.939)

0.921 (0.888–0.954)

KNN

0.908 (0.883–0.933)

< 0.001

0.908 (0.883–0.933)

0.916 (0.883–0.950)

0.900 (0.864–0.936)

0.903 (0.867–0.938)

0.914 (0.880–0.948)

MLP

0.948 (0.929–0.967)

< 0.001

0.948 (0.929–0.967)

0.958 (0.934–0.982)

0.938(0.909–0.968)

0.940 (0.912–0.969)

0.957 (0.932–0.982)

In testing test

GBDT

0.985 (0.966-1.000)

Ref

0.969 (0.940–0.999)

1.000 (1.000–1.000)

0.940 (0.884–0.997)

0.941 (0.885–0.997)

1.000 (1.000–1.000)

LR

0.896 (0.841–0.951)

< 0.001

0.763 (0.691–0.836)

0.828 (0.736–0.921)

0.701 (0.592–0.811)

0.726 (0.624–0.828)

0.810 (0.709–0.911)

AdaBoost

0.940 (0.900–0.980)

0.099

0.939 (0.898–0.980)

0.984 (0.954-1.000)

0.896 (0.822–0.969)

0.900 (0.830–0.970)

0.984 (0.952-1.000)

SVM

0.924 (0.879–0.970)

0.031

0.924 (0.878–0.969)

0.953 (0.901-1.000)

0.896 (0.822–0.969)

0.897 (0.825–0.969)

0.952 (0.900-1.000)

KNN

0.924 (0.878–0.970)

0.030

0.924 (0.878–0.969)

0.938 (0.878–0.997)

0.910 (0.842–0.979)

0.909 (0.840–0.978)

0.938 (0.880–0.997)

MLP

0.916 (0.868–0.964)

0.017

0.916 (0.869–0.964)

0.922 (0.856–0.988)

0.910 (0.842–0.979)

0.908 (0.837–0.978)

0.924 (0.860–0.988)