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28 December 2022 Deep learning algorithm vs XGBoost using Wisconsin breast cancer diagnosis
Wan Aezwani Wan Abu Bakar, Muhamad Amierusyahmi Zuhairi, Mustafa Man, Julaily Aida Jusoh, Nur Laila Najwa Josdi
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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125066Y (2022) https://doi.org/10.1117/12.2663128
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China
Abstract
Breast cancer is causing a significant increase in the number of deaths every year. It is the most common kind of cancer and the leading cause of death in women all over the world. Any improvement in cancer illness prediction and detection is critical for a healthy life. As a result, high accuracy in cancer prediction is critical for keeping patients’ treatment and survival standards up to date. Machine learning and deep learning approaches, which have been shown to have a significant impact on the process of breast cancer prediction and early diagnosis, have become a research hotspot and have been proven to be a powerful technique. We went through one machine learning method, XGBoost and deep neural network in this research on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. After getting the outcomes, these distinct classifiers’ performance is evaluated and compared. The major goal of this research is to use machine-learning and deep learning algorithms to predict and diagnose breast cancer, and to determine which algorithms are the most efficient in terms of confusion matrix, precision, and accuracy.
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Wan Aezwani Wan Abu Bakar, Muhamad Amierusyahmi Zuhairi, Mustafa Man, Julaily Aida Jusoh, and Nur Laila Najwa Josdi "Deep learning algorithm vs XGBoost using Wisconsin breast cancer diagnosis", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125066Y (28 December 2022); https://doi.org/10.1117/12.2663128
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KEYWORDS
Deep learning
Machine learning
Data modeling
Breast cancer
Tumor growth modeling
Education and training
RGB color model
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