Insights into breast cancer: A simple machine learning method for early disease detection
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2025
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| _version_ | 1866902057770811392 |
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| author | Sowah, Anthony Sankoh, Titus Santigie Anku, Vero Bai Jhessim, Eric |
| author_facet | Sowah, Anthony Sankoh, Titus Santigie Anku, Vero Bai Jhessim, Eric |
| contents | <p>Breast cancer remains a significant global health challenge, where accurate prediction plays a vital role in early diagnosis and effective treatment, ultimately saving lives. This study evaluates the performance of three machine learning models; Support Vector Machine (SVM), Random Forest Classifier, and XGBoost for breast cancer prediction. Using the Wisconsin Breast Cancer Dataset, the models were assessed based on their accuracy. The experimental results demonstrated that SVM outperformed the other models, while both XGBoost and the Random Forest Classifier achieved just slightly lower accuracies. This research underscores the potential of machine learning models in enhancing breast cancer prediction and highlights their importance in advancing early detection and treatment strategies.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16916999 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Insights into breast cancer: A simple machine learning method for early disease detection Sowah, Anthony Sankoh, Titus Santigie Anku, Vero Bai Jhessim, Eric Support Vector Machines Random Forest Classifier XGBoost Machine Learning Breast Cancer <p>Breast cancer remains a significant global health challenge, where accurate prediction plays a vital role in early diagnosis and effective treatment, ultimately saving lives. This study evaluates the performance of three machine learning models; Support Vector Machine (SVM), Random Forest Classifier, and XGBoost for breast cancer prediction. Using the Wisconsin Breast Cancer Dataset, the models were assessed based on their accuracy. The experimental results demonstrated that SVM outperformed the other models, while both XGBoost and the Random Forest Classifier achieved just slightly lower accuracies. This research underscores the potential of machine learning models in enhancing breast cancer prediction and highlights their importance in advancing early detection and treatment strategies.</p> |
| title | Insights into breast cancer: A simple machine learning method for early disease detection |
| topic | Support Vector Machines Random Forest Classifier XGBoost Machine Learning Breast Cancer |
| url | https://doi.org/10.5281/zenodo.16916999 |