| Ref. No. | Period | Datasets | Algorithm | Accurateness (%) |
| [16] | 2022 | WDBC and BCCD | SVM, LR, KNN and EC | 99.3%, 98.06%, 97.35%, and 97.61% |
| [3] | 2022 | WDBC | KNN, SVM, LR and Random Forest Tree (RFT) | 91.25%, 92.5%, 93.75% and 95% |
| [17] | 2022 | Regional Oncology Center in Meknes, Morocco. | SVM, KNN, LR and NB | 90.6%, 86.1%, 80.6% and 51.7% |
| [2] | 2021 | WDBC | LR, SVM, KNN, DT Classifier, RF Classifier and NB Classifier. | 98.2%, 98.2%, 96.8%, 91.4%, 97.4% and 97.1% |
| [18] | 2021 | UCSB and BreakHis | c and ANN | 89.1% and 86.27% |
| [19] | 2020 | WDBC | LR and DT | 94.4% and 95.1% |
| [14] | 2020 | (WBC) and (WDBC) | NB, SVM, KNN and LR, | 92%, 96%, 97% and 99% (WBC) and 96%, 94%, 96% and 98% (WDBC) |
| [12] | 2020 | WBC | NB, LR, and Neural Networks (NN) | 95% training and 93% testing and 98% training and 97% testing |
| [20] | 2019 | WDBC | DT and KNN | 92% and 95.95% |
| [13] | 2019 | WBCD | MLP, KNN, CART, Gaussian Naive Bayes (NB) and SVM | 99.12%, 95.61%, 93.85% 94.73% and 98.24% |
| [21] | 2019 | WDBC | Kernel SVM, LR KNN, DT, NB and RF | 98.24%, 96.49%,95.61%,88.59%,85.09% and 92.98% |
| [10] | 2018 | WBC | NB and KNN | 96.19% and 97.51% |
| [14] | 2018 | BCCD and WBCD | DT, SVM, RF, LR, NN DT, SVM, RF, LR, NN | 68.3%, 76.3%, 78.5%, 73.7%, 74.8% (BCCD), 96.3%, 97.7%, 98.9%, 98.1%, 98.5% (WBCD) |
| [22] | 2017 | BCD | NB and KNN | 96.19% and 97.51% |
| [5] | 2016 | WBC | SVM, Bayesian Networks (BN), and RF | 96.6%, 99.2%, and 99.9% |
| [23] | 2013 | WDBC | K-SVM (Hybrid), ACO-SVM, GA-SVM and PSO-SVM | 97.38%, 95.96%, 97.19% and 97.37% |