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Lookup NU author(s): Jianqiao Long, Ziwei Zheng, Chi Ng, Dr Chang Liu, Wenxing Ji
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© 2024 IEEE. Breast cancer is a greatly fatal disease prevalent among women. Early and accurate detection is crucial but challenging, often requiring significant human effort. However, current diagnostic methods have limitations in efficiency and accuracy. This study addresses these challenges by exploring the application of decision tree algorithms for automatic breast cancer classification, aiming to support and enhance the diagnostic process. Using the standard Wisconsin Diagnostic Breast Cancer dataset, our experiments demonstrate that decision trees excel in classification accuracy, outperforming other common machine learning algorithms. This paper thoroughly examines and assesses the performance of the classification model from various viewpoints, highlighting the potential of decision trees to improve diagnostic reliability and efficiency, thereby underscoring their value in clinical settings. The aim is to provide patients with more timely diagnosis and treatment.
Author(s): Long J, Zheng Z, Wang J, Ng CK, Liu C, Ji W
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2024) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM 2024)
Year of Conference: 2024
Pages: 549-554
Online publication date: 16/09/2024
Acceptance date: 02/04/2018
ISSN: 2326-8239
Publisher: IEEE
URL: https://doi.org/10.1109/CIS-RAM61939.2024.10673070
DOI: 10.1109/CIS-RAM61939.2024.10673070
Library holdings: Search Newcastle University Library for this item
ISBN: 9798350364194
