pISSN 1226-6051
eISSN 2508-786X
eISSN 2508-786X
Summary of AI model performance metrics in cancer study
Study name | AI models | Performance metrics | ||||
---|---|---|---|---|---|---|
AUC | Accuracy | Sensitivity | Specificity | F1 score | ||
Aghakhani |
XGBoost | 0.83 | 0.77 | 0.74 | 0.77 | 0.8 |
Ahamad |
RF | Medical data 1.00, 0.98a AE data 1.00+ |
Medical data 1.00, 0.98a AE data 1.00+ |
N/A | N/A | Medical data 1.00, 0.98a AE data 1.00+ |
Banoei |
BNN | 0.85 | N/A | 0.57 | 0.94 | N/A |
Carbonell |
Elastic Net | 0.78, 0.82a | N/A | N/A | N/A | N/A |
An |
LASSO | 0.83 | 0.86 | 0.94 | 0.90 | N/A |
Gao |
Ensemble model | 0.99 | 0.96 | 0.87 | 0.97 | 0.87 |
Experton |
RF | 0.71, 0.66b | 0.65, 0.61b | N/A | N/A | N/A |
Heydar |
RF | DM 0.80 non-DM 0.84 |
DM 0.82 non-DM 0.80 |
DM 0.80 non-DM 0.91 |
DM 0.55 non-DM 0.56 |
N/A |
Heyl |
RF | 0.90 | 0.83 | N/A | N/A | N/A |
Hilal |
XGBoost | Delta 0.78, 0.81b Omicron 0.70, 0.78b |
Delta 0.96, 0.85b Omicron 0.98, 0.94b |
N/A | N/A | Delta 0.27, 0.35b: Omicron 0.27, 0.34b |
Ikemura |
GBM | 0.80 | N/A | 0.919 | 0.735 | N/A |
Jamshidi |
RF | 0.79 | N/A | 0.70 | 0.75 | N/A |
Edqvist |
GBM, RF | N/A | T1DM RF: 0.88 T2DM GBM: 0.74 |
N/A | N/A | N/A |
Karasneh |
LR | 0.77 | N/A | N/A | N/A | N/A |
Lee |
LR | 0.88 | N/A | N/A | N/A | N/A |
Modelli de Andrade |
Elastic Net | 0.78 | N/A | N/A | N/A | N/A |
Kivrak |
XGBoost | N/A | 0.99 | 0.99 | 1.00 | N/A |
Rahman |
self-developed model | 0.95 | 0.90 | 0.80 | 0.92 | N/A |
Lorè |
DT | 0.73 | N/A | N/A | N/A | N/A |
Rasmy |
self- developed model | 0.93, 0.92c | N/A | N/A | N/A | N/A |
Wollenstein-Betech |
LR | 0.63, 0.74b | 0.79, 0.71b | N/A | N/A | 0.71, 0.70b |
Schmidt |
XGBoost | 0.79 | N/A | N/A | N/A | N/A |
Alle |
LR | 0.92 | N/A | N/A | N/A | 0.71 |
Nojiri |
Lasso | 0.80, 0.78a | N/A | N/A | N/A | N/A |
Snider |
DT | 0.93, 0.96a | N/A | N/A | N/A | N/A |
Kar |
XGBoost | 0.88 | 0.97 | 0.78 | 0.98 | 0.81 |
Wu |
self- developed model | 0.85 | 0.75 | 0.79 | 0.74 | 0.40 |
Guan |
XGBoost | 1.00 | N/A | N/A | N/A | 0.94 |
Jung |
XGBoost | 0.65 | 0.70 | N/A | N/A | N/A |
Zhao |
SVM | 0.94 | 0.91 | 0.90 | 0.94 | N/A |
Jiao |
self- developed model | 0•84 | N/A | 0.73 | 0.85 | 0.83 |
Kang |
NN | 0.95 | N/A | 1.00 | 0.85 | 0.96 |
Wong |
XGBoost | 0.81, 0.72a | N/A | N/A | N/A | N/A |
Rojas-García |
XGBoost | 0.79 | 0.75 | 0.83 | 0.74 | 0.48 |
Burns |
XGBoost | 0.75 | 0.67 | N/A | 0.66 | 0.49 |
Wang |
self- developed model | 0.85 | 0.83 | 0.62 | 0.89 | N/A |
Chen |
RF | 0.90 | 0.94 | 0.99 | 0.93 | 0.97 |
De Freitas |
RF | 0.93 | 0.90 | N/A | N/A | 0.94 |
Jehi |
self- developed model | 0.90 | N/A | N/A | N/A | N/A |
Hao |
RF | 0.88b, 0.85c | 0.88b, 0.86c | N/A | N/A | 0.91b, 0.91c |
Aminu |
SVM, LR | 1.00 | 0.99 | 1.00 | 0.98 | N/A |
Chen |
LR | 0.81 | N/A | 0.80 | 0.71 | N/A |
*all values of predicting mortality except for a: prediction value of severity, b: prediction value of hospitalization, and c: prediction value of mechanical ventilation; +: values including mortality and severity; Lasso: Least Absolute Shrinkage and Selection Operator; XGBoost: Extreme Gradient Boosting; RF: Random Forest; LR: Logistic Regression; DT: Decision Tree; SVM: Support Vector Machine; NN: Neural Network; GBM: Gradient Boosting Machine; AUC: area under the curve; AI: artificial intelligence