Paper | Algorithms | Result | Dataset | Proposed Model Result |
[4] | Random forest, Logistic regression, SVM | Accuracy (80.75%, 80.88%, 82%) | IBM Waston | AUC for SVM AUC (87%) |
[21] | Naive Bayes | AUC (83%) | IBM Waston | AUC for Naive Bayes same (83%) |
[22] | Decision trees, Logistic regression, Neural networks and SVM | Accuracy (62.98, 61.65, 61.40 and 61.78) | Cell2cell | ACC for decision trees (98%) and SVM (99%) |
[23] | GP-AdaBoost | AUC (0.91) | Cell2cell | The best AUC for SVM (99%) AdaBoost not used |
[24] | C4.5 decision tree | AUC (63.04) | Cell2cell | AUC decision trees (98%) |
[25] | SVM | AUC (94.13) | Cell2cell | AUC for SVM (99%) |