Breast Cancer Classification using XGBoost
1 Department of Computer Science, North Dakota State University, Fargo, North Dakota, ND 58105, USA.
2 Department of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh.
2 School of Business, San Francisco Bay University, Fremont, CA 94539, USA.
3 Department of Computer Science, Morgan State University, Baltimore, Maryland 21251, USA.
4 Department of Computer Science, School of Engineering, San Francisco Bay University, Fremont, CA 94539, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 1985–1994
Article DOI: 10.30574/wjarr.2024.21.2.0625
Publication history:
Received on 13 January 2024; revised on 22 February 2024; accepted on 24 February 2024
Abstract:
Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary classification (normal/abnormal). Artificial intelligence (AI) has been employed to diagnose breast tumors swiftly and accurately at an early stage. This study employs the Extreme Gradient Boosting (XGBoost) machine learning technique for the detection and analysis of breast cancer. XGBoost provides an accuracy of 94.74% and recall of 95.24% on Wisconsin breast cancer Wisconsin (diagnostic) dataset.
Keywords:
Training; Machine learning; Breast cancer; Data models; Classification algorithms; XGBoost; Feature Importance; Computer Aided Diagnosis; Artificial Intelligence
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0