Table 4.

Reported performance measures of the ML^a models.

Author and ML model		Classification measurements						Calibration measurements				Other
		Specificity	PPVb/precision	Recall/sensitivity	F1 score	Accuracy	HLc score		Brier score	Calibration curve
Pirrachio et al [1]
	Ensemble SLd-1	N/Ae	N/A	N/A	N/A	N/A	N/A		0.079	Uf=0.0007 (calibration plot)	DSg=0.21
	Ensemble SL-2	N/A	N/A	N/A	N/A	N/A	N/A		0.079	U=0.006 (calibration plot)	DS=0.26
Nielsen et al [24]
	NNh	N/A	0.388	N/A	N/A	N/A	N/A		N/A	N/A	Mathews correlation coefficient
Purushotham et al [27]
	NN	N/A	N/A	N/A	N/A	N/A	N/A		N/A	N/A	0.491 (AUPRCi)
	Ensemble	N/A	N/A	N/A	N/A	N/A	N/A		N/A	N/A	0.435 (AUPRC)
Nimgaonkar et al [25]
	NN-15 features	N/A	N/A	N/A	N/A	N/A	27.7		N/A	Calibration plot	N/A
	NN-22 features	N/A	N/A	N/A	N/A	N/A	22.4		N/A	Calibration plot	N/A
Xia et al [26]
	Ensemble-LSTMj	0.7503	0.294	0.7758	0.4262	0.7533	N/A		N/A	N/A	N/A
	LSTM	0.7746	0.305	0.7384	0.4317	0.7703	N/A		N/A	N/A	N/A
	RFk	0.7807	0.306	0.71197	0.4290	0.7734	N/A		N/A	N/A	N/A
Nanayakkara et al [28]
	RF	0.79	0.75	0.76	N/A	0.78	N/A		0.156	Calibration plot	0.47 (log loss)
	SVCl	0.81	0.77	0.75	N/A	0.78	N/A		0.153	Calibration plot	0.47 (log loss)
	GBMm	0.78	0.75	0.8	N/A	0.79	N/A		0.147	Calibration plot	0.45 (log loss)
	NN	0.72	0.71	0.82	N/A	0.77	N/A		0.158	Calibration plot	0.48 (log loss)
	Ensemble	0.81	0.77	0.77	N/A	0.79	N/A		0.148	Calibration plot	0.45 (log loss)
Meyer et al [29]
	RNNn	0.91	0.9	0.85	0.88	0.88	N/A		N/A	N/A	N/A
Meiring et al [7]
	DTo, NN, SVMp	N/A	N/A	N/A	N/A	N/A	N/A		N/A	N/A	N/A
Lin et al [30]
	RF	N/A	N/A	N/A	0.459	0.728	N/A		0.085	Calibration plot	N/A
	NN	N/A	N/A	N/A	0.406	0.666	N/A		0.091	Calibration plot	N/A
	SVM	N/A	N/A	N/A	0.460	0.729	N/A		0.086	Calibration plot	N/A
Krishnan et al [31]
	ANN-ELMq	N/A	N/A	0.98	0.98	0.98	N/A		N/A	N/A	Mathews correlation coefficient
Kang et al [32]
	k-NNr	N/A	N/A	N/A	0.745	0.673	N/A		N/A	Calibration plot	N/A
	SVM	N/A	N/A	N/A	0.752	0.696	N/A		N/A	Calibration plot	N/A
	RF	N/A	N/A	N/A	0.762	0.69	N/A		N/A	Calibration plot	N/A
	XGBs	N/A	N/A	N/A	0.763	0.711	N/A		N/A	Calibration plot	N/A
	NN	N/A	N/A	N/A	0.749		N/A		N/A	Calibration plot	N/A
Johnson et al [33]
	LRt univariate	N/A	N/A	N/A	N/A	N/A	22		0.051	N/A	N/A
	LR multivariate	N/A	N/A	N/A	N/A	N/A	19.6		0.048	N/A	N/A
Holmgren et al [34]
	NN	N/A	N/A	N/A	N/A	N/A	N/A		0.106	Calibration plot	N/A
Garcia-Gallo et al [35]
	SGBu	N/A	N/A	N/A	N/A	0.725	0.0916		N/A	Calibration plot	N/A
	SGB-LASSOv	N/A	N/A	N/A	N/A	0.712	0.0916		N/A	Calibration plot	N/A
El-Rashidy et al [36]
	Ensemble	0.94	N/A	0.911	0.937	0.944	N/A		N/A	N/A	N/A
Silva et al [37]
	NN	0.79	N/A	0.78	N/A	0.7921	N/A		N/A	N/A	N/A
Caicedo-Torres et al [38]
	NN	0.827	N/A	0.75	N/A	N/A	N/A		N/A	N/A	N/A
Deshmukh et al [39]
	XGB	0.27	N/A	1	N/A	N/A	N/A		N/A	N/A	N/A
Ryan et al [40]
	XGB	0.75	N/A	0.801	0.378	0.75	N/A		N/A	N/A	N/A
Mayaud et al [41]
	GAw+LR	N/A	N/A	N/A	N/A	N/A	10.43		N/A	Calibration plot	N/A

^aML: machine learning.

^bPPV: positive predictive value.

^cHL: Hosmer-Lemeshow.

^dSL: super learner.

^eN/A: not available.

^fU statistics.

^gDS: discrimination slope.

^hNN: neural network.

ⁱAUPRC: area under the precison-recall curve.

^jLSTM: long short-term memory.

^kRF: random forest.

^lSVC: support vector classifier.

^mGBM: gradient boosting machine.

ⁿRNN: recurrent neural network.

^oDT: decision tree.

^pSVM: support vector machine.

^qANN-ELM: artificial neural network extreme learning machine.

^rk-NN: k-nearest neighbor.

^sXGB: extreme gradient boosting.

^tLR: logistic regression.

^uSGB: stochastic gradient boosting.

^vLASSO: least absolute shrinkage and selection operator.

^wGA: genetic algorithm.