The following machine learning algorithms were applied to classify the data:
K-Nearest Neighbors (KNN):
The KNN algorithm was utilized to classify the data based on the majority vote of its neighbors. The model's performance was evaluated using the ROC curve.
Decision Tree:
Decision trees were used to model the decisions and possible consequences. The ROC curve was also used to evaluate this model's performance.
Support Vector Machine (SVM):
The SVM algorithm was applied to find the optimal hyperplane that separates the data into classes. The ROC curve was used for performance evaluation.
Ensemble Learning Models:
Boosting: This approach was used to improve the model's accuracy by combining the predictions of multiple weak learners to form a strong learner. Bagging with SVM: Bagging was combined with SVM to reduce variance and avoid overfitting, providing better stability and accuracy.
Random Forest:
This ensemble learning method was employed to enhance prediction accuracy and control overfitting by averaging the results of various decision trees.
Results: Among all models, the Bagging with SVM approach yielded the highest accuracy for this dataset, demonstrating superior performance in terms of precision and reliability for breast cancer detection.