Breast cancer classification using machine learning methods
| dc.contributor.author | Filali Sabir, Salem Abdelhamid | |
| dc.date.accessioned | 2023-10-02T10:19:17Z | |
| dc.date.available | 2023-10-02T10:19:17Z | |
| dc.date.issued | 2023-10-02 | |
| dc.description.abstract | Breast cancer is a significant health concern, and early detection is crucial for effective treatment. Machine learning classification techniques have shown great efficiency in improving breast cancer diagnosis. In this research, we used five different algorothims : Random Forest (RF), Logistic Regression (LR), XGBoost, Support Vector Machine (SVM), and Decision Tree (DT) for breast cancer classification. The dataset used was the Wisconsin Diagnostic Breast Cancer dataset (WDBC).it is observed that the logistic regression outperform all other classifiers and achieves impressive scores across multiple performance metrics such as specifity of 100% , precision of 100% , sensitivity of 98.63% and Accuracy of 99.12% .As we conducted a thorough comparison with previous approaches, and our results demonstrated the superiority of our proposed model in breast cancer classification. | en_US |
| dc.identifier.other | EL/2023 | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/41005 | |
| dc.language.iso | fr | en_US |
| dc.publisher | University of M'sila | en_US |
| dc.subject | Breast Cancer classification , Random Forest (RF), Logistic Regression (LR), XGBoost, Support Vector Machine (SVM), and Decision Tree (DT). | en_US |
| dc.title | Breast cancer classification using machine learning methods | en_US |
| dc.type | Thesis | en_US |