| [8] |
KNN, SVM, DT, MLP |
WBCD and WDBC |
Accuracy: 98.12%, Precision: 99.2%, Recall: 97.85% |
Authors have limited performance parameters. More parameters should be employed including the Info Gain test, Gain Ratio test, and Chi-square tests |
| [9] |
MLP, SVM, DNN, RNN |
Breast Cancer Relapse Dataset (BCRD) |
Accuracy: 94.53% |
The rough neural network results in the lowest accuracy in comparison to other methods. |
| [10] |
ML-DSS, RO |
SEER |
Accuracy: 86.0%, F-measure: 69.8%, Sensitivity: 67.1%, Specificity: 88.4%, AUC: 0.822 |
There is a need to improve the precision of the model through the weighting relative importance of the attributes by making a hybrid approach of ML algorithms and the models of RO. |
| [11] |
MLP, SVM, SMO |
WBCD |
Accuracy: 96.99%, Precision: 97%, Recall: 97%, AUC: 0.968 |
More ML techniques should be considered to achieve enhanced predictive outcomes. |
| [12] |
DT, SVM, MLP, KNN, LR, RF |
Coimbra Breast Cancer Dataset (CBCD) |
Accuracy: 100%, Precision: 100%, Recall: 100%, F1-score: 100% |
The authors should have employed more models and parameters |
| [13] |
MLP, CNN, SVM |
EHRs |
Precision: 81.47%, Recall: 77.82%, F1-score: 79.42%, AUC: 0.9489 |
The heterogeneity problem in clinical narratives should be addressed in the study. |
| [14] |
SVM, ANN, NB, LDA |
WDBC |
Accuracy: 98.82%, Sensitivity: 98.41%, Specificity: 99.07%, AUC: 0.9994 |
SVM-LDA and NN-LDA outperform the other ML classifier models, but, NN-LDA is not chosen because of its longer time for computational. |
| [15] |
LR, SVM, KNN, and PCA |
UCI Sourced |
Accuracy: 92.78%, Precision: 96.55%, Sensitivity: 91.07%, Specificity: 95.14% |
This research work considers fewer variables in prediction. |
| [16] |
LR, KNN, SVM, DT, RF, GBDT, MLP, XGBoost and Ensemble Learning |
NFSC |
Accuracy: 91.62%, Recall: 90.28%, F1-score: 89.39% |
The authors have only designed the framework of a system and adopted existing methods. |
| [17] |
ANN, KNN, SVM, NBC, COX |
TCGA |
Accuracy: 98.82%, Sensitivity: 100%, Specificity: 100%, PPV: 100%, NPV: 99.08%, AUROC: 99.81% |
This research can be explored by designing the model of two-level or multi-level which will provide the effects of contextual volume of surgeon and hospital on the recurrence of breast cancer. |
| [18] |
LR, NB, KNN, SVM and PCA |
WPBC |
Accuracy: 80%, Precision: 80%, Recall: 62%, F1-score: 76%, AUROC: 0.81, AUPRC: 0.62 |
SVM performance on imbalanced datasets is not very effective whereas on balanced datasets it is effective. |
| [19] |
J48 DT, NB, LR, SVM, KNN, MLP, PART, OneR, RF and TF-IDF |
KAUH sourced dataset |
Accuracy: 92.25%, Sensitivity: 92.3%, Specificity: 88.7% |
The unstructured and clinical variable format of data stored in the HER hospital increases the variability and complexity of their extraction. |
| [20] |
LR, XGBoost, MLP, NB, RF, KNN, DT |
WBCD |
Accuracy: 98.3%, AUC: 99.3%, Precision: 96.6%, Recall: 97%, F1- score: 96.7% |
This proposed work showed limited performance due to the imbalanced and small size of the dataset which leads to low prediction as compared with the classification of cancer on the other two datasets. |
| WDBC |
Accuracy: 99.2%, AUC: 99.5%, Precision: 97.4%, Recall: 97.4%, F1- score: 97.4% |
| WPBC |
Accuracy: 78.6%, AUC: 78.9%, Precision: 77.7%, Recall: 77.2%, F1- score: 78% |
| [21] |
DT, LDA, LR, SVM, ET, PNN DNN, and RNN |
NIH sourced dataset |
Accuracy: 98.7%, Precision: 96.7%, Recall: 76.4%, F1-score: 85.2% |
This proposed work would be more confirm the accuracy of the techniques of classification in the prediction of breast cancer considering the feature selection technique. |
