Editorial
A Comprehensive Feature Engineering Approach for Breast Cancer Dataset
@ARTICLE{10.4108/eetpht.10.5327, author={Shambhvi Sharma and Monica Sahni}, title={A Comprehensive Feature Engineering Approach for Breast Cancer Dataset}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Breast Cancer, Univariate Analysis, Bivariate Analysis, Heat Map, Correlation}, doi={10.4108/eetpht.10.5327} }- Shambhvi Sharma
Monica Sahni
Year: 2024
A Comprehensive Feature Engineering Approach for Breast Cancer Dataset
PHAT
EAI
DOI: 10.4108/eetpht.10.5327
Abstract
Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.
Copyright © 2024 S. Sharma et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
