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Breast Cancer Classification using LGBM and SVM
1 Department of Electrical and Electronic Engineering, Southern University Bangladesh, Chattogram, Bangladesh.
2 Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh.
3 Department of Electrical and Electronic Engineering, Leading University, Sylhet, Bangladesh.
4 Department of CSE, United International University, Dhaka, Bangladesh.
5 Institute of Biological Sciences, University of Rajshahi, Rajshahi, Bangladesh.
Research Article
International Journal of Science and Research Archive, 2022, 07(02), 876-881.
Article DOI: 10.30574/ijsra.2022.7.2.0313
Publication history:
Received on 07 November 2022; revised on 19 November 2022; accepted on 28 November 2022
Abstract:
Breast cancer (BC) is among the most common cancers affecting women worldwide, highlighting the urgent need for early and accurate diagnosis. It develops in the breast tissue and is one of the most frequent causes of women’s death. This cancer can be cured if it is diagnosed at preliminary stage. Malignant and benign are two types of tumor found in case of breast cancer. Malignant tumors are deadly as their rate of growth is much higher than benign tumors. So, early identification of tumor type is pivotal for the appropriate treatment of a patient having breast cancer. Machine learning (ML) has emerged as a powerful tool for BC classification, enhancing diagnostic precision and improving patient outcomes. In this work, Wisconsin Breast Cancer Dataset is used. Our goal is to analyze the dataset and evaluate the performance of LGBM and SVM for predicting breast cancer.
Keywords:
Breast Cancer Diagnosis; WBCD Dataset; Malignant; Benign; Classification; Machine Learning; LGBM; SVM
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
