Table 6.

Matrix of Studies with Relative Medical Conditions to Compare with The Study Results

Article	Authors	Year	Country	Setting	Medical Condition	Data Balance	Model Evaluation	Classifier	AUC
Predictors of in-hospital length of stay among cardiac patients: A machine learning approach	Daghistani et al39	2019	Saudi Arabia	King Abdulaziz Medical City Complex in Riyadh	Predict LoS for cardiac patients	Smote	Cross-validation	Random Forest	0.94
Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre- Incision Variables	LaFaro et al41	2015	USA	New York Medical College	Predict ICU LoS after cardiac surgery	–	Cross-validation	Ensemble of Neural Network	0.90
Using machine learning for predicting severe postoperative complications after cardiac surgery	Lapp et al57	2018	UK	Golden Jubilee National Hospital	Predict complications after cardiac surgery	–	–	Random Forest	0.71
Prediction of In-Hospital Mortality And Length of Stay in Acute Coronary Syndrome Patients Using Machine-Learning Methods	Yakovlev et al40	2018	Russia	–	Predict mortality and LoS for acute coronary syndrome patients	–	Cross-validation	Naïve Bayes	0.90
This study		2019	Saudi Arabia	Saud Albabtain Cardiac Center	Predict LoS for iCABG patients	Both method	Cross-validation	Random Forest	0.81