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Table 8 Comparison of the proposed scheme with recently published ML models to predict COVID-19 patients' mortality risk

From: Prognosis patients with COVID-19 using deep learning

Algorithm Input features dataset Key performance indicators (KPI)
MPCD Recall/sensitivity F2 score Precision AUC Accuracy
DL 48 Clinical data 0.93 1 0.93 0.91 0.93 0.95
RF 0.88 0.95 0.89 0.85 0.89 0.93
SVM   0.77 0.91 0.87 0.87 0.87 0.89
ANN   0.78 0.89 0.9   0.88 0.90
XGBoost   0.81 0.93 0.94 0.90 0.90 0.91
LR   0.76 0.90 0.87 0.88 0.86 0.89
SVM and KNN [21] 11 Clinical data 0.80
ANN [24] 42 Clinical data 0.90
ML [27] 12 Clinical data 0.90 0.866
DNN [28] 51 Clinical data 0.8125 0.97 0.9598
ML [29] 20 Clinical data 0.94
Multivariate Analysis [23] (Cox proportional regression) 4 Clinical data 0.95 0.91
ML [34] 3 Clinical data 0.91
Multivariate Regression model [35] 7 Clinical data 0.74
CNN and Deep Transfer Learning [30] RGB X-ray images 0.9762 0.8810
CNN and Deep Transfer Learning [31] X-ray & CT-Scan images 0.94 0.95 0.95
Deep CNN-LSTM [54] X-ray Images 0.993 0.999 0.994
CNN- Ensemble of Machine Learning [57] X-ray Images 0.978 1 0.989
CNN-RNN [53] X-ray Images 0.999 0.999 0.999 0.999
KNN [84] Clinical data   1.00 0.93 0.942 0.922 0.9374