Research Article
Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques
Table 6
Comparison of machine learning algorithms on Wisconsin Breast Cancer dataset.
| Author | Year | Dataset | Imbalance handling | Feature selection | Features | Classifier | Validation type | Accuracy achieved (%) |
| Aryal & Paudel [37] | 2020 | WDBC | — | — | 30 | Gradient Boosting | 10-fold | 98.88% | Ahmet Saygili [38] | 2018 | WDBC | — | Gain Ratio | 24 | Random Forest | 10-fold | 98.77% | Dubey et al. [39] | 2016 | WDBC | — | — | — | -means clustering | — | 92.00% | Salama et al. [41] | 2016 | WDBC | — | — | 30 | SMO | 10-fold | 97.71% | Our approach | 2020 | WDBC | Normalization by standardization | Correlation-based selection & RFE | 11 | MLP | 5-fold | 99.12% |
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