Evaluating the Generalizability of Support Vector Machine for Breast Cancer Detection
Keywords:
Malignant, Benign, Support Vector Machine, Classifiers, Evaluation Metrics, Breast CancerAbstract
Breast cancer is caused by abnormal cell growth in the breast. Early detection has been observed to be crucial for successful treatment. Accurate detection methods are essential. Machine learning models, particularly Support Vector Machines (SVMs), have shown promise. However, concerns exist regarding their generalizability across real-world scenarios with varying software environments and data processing techniques. This research investigates this gap by comparing SVM performance with other classifiers such as Naïve Bayes, Random Forest, Multilayer Perceptron and Decision Tree. These classifiers were tested on the Wisconsin Breast Cancer dataset using both the Waikato Environment for Knowledge Analysis (WEKA) and Jupyter Notebook. The study recorded performance metrics such as accuracy, precision, recall, and f1_score. After the analysis, it was observed that in WEKA, Support Vector Machine under the 10-fold cross-validation and 70% split, had the highest accuracies of 0.981 and 0.977 respectively. Interestingly, Multilayer Perceptron also achieved an accuracy of 0.977 under the 70% split. In the Jupyter Notebook, Support Vector Machine also produced the highest accuracy value of 0.99 under the 70% split. However, Random Forest produced the highest accuracy of 0.97 which was closely followed by Support Vector Machine which had a value of 0.96 in the 10-fold cross-validation.
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