Abstract

One of the most contagious illnesses and the second-leading cause of cancer-related death in women is breast cancer. Early detection of tumor is critical for providing healthcare providers with useful clinical information which can help them make a more accurate diagnosis. To accurately diagnose breast cancer, a computer-aided detection (CAD) system that employs machine learning is required. The paper proposes web based tumor prediction system which analyzes different machine learning algorithms for breast tumor classification to determine the best performing model. Different evaluation criteria namely accuracy, ROC AUC, etc are mostly employed for evaluating models but they make the selection of the best model strenuous. A multi-criteria decision making (MCDM) approach has been employed for selecting the best performing model. Further, a web-based portal has been developed to provide the user interface for this functionality.

Details

Title
Breast Tumor Classification using Machine Learning
Author
Siddiqui, Salman  VIAFID ORCID Logo  ; Mallick, Mohd Usman; Varshney, Ankur
Section
Research article
Publication year
2024
Publication date
Jan 2024
Publisher
European Alliance for Innovation (EAI)
e-ISSN
24090026
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3273809690
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.