Breast Cancer Classification using LGBM and SVM
Denesh Dasa✉, Md Masum Billahb, Amit Deb Nathc d, Numair Bin Sharif, Kallol Kanti Mondale
- aDepartment of Electrical and Electronic Engineering , Southern University Bangladesh , Chattogram , Bangladesh
- bDepartment of Electrical and Electronic Engineering , University of Rajshahi , Rajshahi , Bangladesh
- cDepartment of CSE , United International University , Dhaka , Bangladesh
- dDepartment of Electrical and Electronic Engineering , Leading University , Sylhet , Bangladesh
- eInstitute of Biological Sciences , University of Rajshahi , Rajshahi , Bangladesh
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
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.