Breast cancer classification using machine learning methods
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Date
2023-10-02
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University of M'sila
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.
Description
Keywords
Breast Cancer classification , Random Forest (RF), Logistic Regression (LR), XGBoost, Support Vector Machine (SVM), and Decision Tree (DT).