https://github.com/saeedanbarimoghadam/breast-cancer-detection-using-machine-learning-algorithms
https://github.com/saeedanbarimoghadam/breast-cancer-detection-using-machine-learning-algorithms
boosting-algorithms datamining decision-tree-classifier decision-trees knn-classification machine-learning machine-learning-algorithms random-forest random-forest-classifier svm-model
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/saeedanbarimoghadam/breast-cancer-detection-using-machine-learning-algorithms
- Owner: Saeedanbarimoghadam
- Created: 2024-09-06T13:07:05.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-21T10:08:29.000Z (8 months ago)
- Last Synced: 2024-11-06T06:41:53.742Z (7 months ago)
- Topics: boosting-algorithms, datamining, decision-tree-classifier, decision-trees, knn-classification, machine-learning, machine-learning-algorithms, random-forest, random-forest-classifier, svm-model
- Language: Jupyter Notebook
- Homepage:
- Size: 1.62 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Breast Cancer Detection Using Machine Learning Algorithms
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​.