|
ML category
|
Number of Studiesa
|
Mean (STD)
|
Median
|
Min
|
Max
|
IQR
|
|---|
|
NN
|
15
|
0.71 (0.07)
|
0.71
|
0.61
|
0.81
|
0.64–0.78
|
|
Boosted treea
|
17
|
0.70 (0.06)
|
0.7
|
0.59
|
0.81
|
0.66–0.75
|
|
RF
|
16
|
0.68 (0.09)
|
0.64
|
0.53
|
0.9
|
0.63–0.72
|
|
DT
|
9
|
0.70 (0.10)
|
0.67
|
0.59
|
0.88
|
0.63–0.77
|
|
Regularized Logistic Regressionb
|
16
|
0.69 (0.08)
|
0.65
|
0.58
|
0.84
|
0.64–0.75
|
|
SVM
|
10
|
0.70 (0.11)
|
0.68
|
0.5
|
0.86
|
0.65–0.78
|
|
Other ML algorithmsc
|
10
|
0.68 (0.04)
|
0.68
|
0.62
|
0.77
|
0.66–0.71
|
- Abbreviations: ML machine learning, NNs neural networks, RF random forest, DT decision tree, SVM support vector machine, STD standard deviation, IQR the interquartile range. athe total number of studies is larger than total number of included studies, because some studies used more than 1 ML algorithms. aIt includes adaboost, gradient boosting, gradient descent boosting, boosting, XGBoost; bIt includes Lasso (L1 regularization), ridge regression (L2 regularization), and elastic-net algorithms; cIt includes: DT ensembled with SVM, RF combined with SVM, tree-augmented naïve Bayesian network
