An integrative machine learning framework for classifying SEER breast cancer.

ABSTRACT: Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to

SUBMITTER: Manikandan P 

PROVIDER: S-EPMC10067827 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature