Author(s): W. Abdul Hameed, D. Anuradha, S. Kaspar

Email(s): anuradhadhanapal1981@gmail.com

DOI: 10.52711/0974-360X.2021.01094   

Address: W. Abdul Hameed, D. Anuradha*, S. Kaspar
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamilnadu, India.
*Corresponding Author

Published In:   Volume - 14,      Issue - 12,     Year - 2021


ABSTRACT:
Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.


Cite this article:
W. Abdul Hameed, D. Anuradha, S. Kaspar. Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer. Research Journal of Pharmacy and Technology. 2021; 14(12):6330-4. doi: 10.52711/0974-360X.2021.01094

Cite(Electronic):
W. Abdul Hameed, D. Anuradha, S. Kaspar. Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer. Research Journal of Pharmacy and Technology. 2021; 14(12):6330-4. doi: 10.52711/0974-360X.2021.01094   Available on: https://rjptonline.org/AbstractView.aspx?PID=2021-14-12-24


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