Table 1.

Significant scholarly works that ML techniques to compare with the performance of APACHE II

Study	Data source	Condition	Number of patients	Machine learning algorithms	Accuracy	AUC
Samaneh Layeghian Javan et al.[19]	MIMIC III	Cardiac arrest	4611	Stacking algorithm	0.76	0.82
Min Woo Kang et al.[20]	Seoul National University Hospital	Continuous renal replacement therapy	1571	Random forest	/	0.78
Meng Hsuen Hsieh et al. [21]	Chi-Mei Medical Center	Patients with unplanned extubation in intensive care units	341	Random forest	0.88	0.91
Zhongheng Zhang et al.[22]	SAILS study and OMEGA study	Acute respiratory distress syndrome	1071	Neural network	/	0.821
Dan Assaf et al.[21]	Sheba Medical Center	Coronavirus disease (COVID-19)	162	Random forest	0.92	0.93
Grupo de Trabajo Gripe A Grave et al.[23]	GETGAG/SEMICYUC database	Severe influenza	3959	Random forest	0.83	0.82
Kuo-Ching Yuan et al.[24]	Taipei Medical University Hospital	Sepsis	434	XGBoost	0. 82	0.89
Scherpf M et al.[39]	MIMIC III	Sepsis	1050	Recurrent neural network	/	0.81
Zhang Z et al.[40]	MIMIC III	Acute kidney injury	6682	XGBoost	/	0.86
Kong G et al.[41]	MIMIC III	Sepsis	16,688	Gradient boosting machine		0.85
Our work	MIMIC III	ICU patients	24,777	XGBoost	0.87	0.81