Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques

Meshwa Rameshbhai Savalia and Jaiprakash Vinodkumar Verma
Additional contact information
Meshwa Rameshbhai Savalia: Institute of Technology, Nirma University, India
Jaiprakash Vinodkumar Verma: Institute of Technology, Nirma University, India

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2023, vol. 12, issue 1, 1-19

Abstract: Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJRQEH.318483 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:12:y:2023:i:1:p:1-19

Access Statistics for this article

International Journal of Reliable and Quality E-Healthcare (IJRQEH) is currently edited by Anastasius Moumtzoglou

More articles in International Journal of Reliable and Quality E-Healthcare (IJRQEH) from IGI Global
Bibliographic data for series maintained by Journal Editor ().