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Table 2 Descriptive statistics of AUC by ML category

From: Application of machine learning in predicting hospital readmissions: a scoping review of the literature

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
  1. 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