W. Abdul Hameed, D. Anuradha, S. Kaspar
W. Abdul Hameed, D. Anuradha*, S. Kaspar
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamilnadu, India.
Volume - 14,
Issue - 12,
Year - 2021
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
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
1. Arjun Patidar SC, Shivhare, Umesh Ateneriya, Sonu Choudhary. A Comprehensive Review on Breast Cancer. Asian J. Nursing Edu. and Research.2012; 2(1): 28-32.
2. Jean Tresa J. Prevention and Management of Lymphedema in Patients with Breast Cancer. International Journal of Nursing Education and Research.2015; 3(1): 101-102.
3. Jayashree V, Malarkodi Velraj. Breast Cancer and various Prognostic Biomarkers for the diagnosis of the disease: A Review. Research J. Pharm. and Tech. 2017; 10(9): 3211-3216.
4. Thaer Ali Hussein, Ibrahim A. Al Tamemi. Micro RNA 145 as Biomarker for Breast cancer. Research J. Pharm. and Tech. 2019; 12(12): 5923-5926.
5. Muthulakshmi, Ramya R. A Study to assess the knowledge of Breast Cancer and awareness of Mammography among women (30-50) in Saveetha Medical College and Hospital. Research J. Pharm. and Tech. 2018; 11(10): 4219-4221.
6. Geetha M, Menaka K, Padmavathi P. Awareness of Breast self-examination and risk factors of Breast Cancer among Women. Asian J. Nur. Edu. and Research.2017; 7(3): 413-416.
7. Sampoornam W. Stress and Quality of Life among Breast Cancer Patients. Asian J. Nur. Edu. & Research.2014; 4(3): 325-327.
8. Sampoornam W. Coping to Alleviate Stress and Improve Quality of Life among Breast Cancer Patients. Int. J. Nur. Edu. and Research. 2016; 4(4): 427-428.
9. Nayna Abhang, Joe Lopez. Health Belief Model for Social Marketing of Breast Self-Examination – A Review of Literature. Asian Journal of Management. 2018; 9(1):493-499.
10. Sara jawad kadhem, AbdulSahib K. Ali, Abdul-Ameer N. AL rikabi. Compartment Between Breast Cancer Patient Undergoing Chemotherapy and Healthy Women by Cytokinesis-block Micronucleus test and Nuclear Division Index. Research J. Pharm. and Tech. 2018; 11(2):705-710.
11. Vomweg TW, Buscema M, Kauczer HU, Teifke A, Intraliga M, Terzi S, Heussel P, Achenbach T, Rieker O, Mayer D, Thelen M. Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography. Med. Phys.2003; 30: 2350-59.
12. Wolberg WH, Mangasarian OL. Multi-surface method of pattern separation for medical diagnosis applied to breast lump. Proceedings of the National Academy of Science USA.1990; 87: 23.
13. Carpenter GA, Markuzon N. ARTMAP-1C and medical diagnosis … Neural network .1998; 11(2): 323-336.
14. McCullogh WS, Pitts W. Bull Maths Biophysics.1943; vol. 5, pp. 115-118.
15. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci.1982: pp. 2554-2558.
16. Kohonen T. Self-organized formation of topologically correct feature maps. Biological cybernetics.1982; vol. 43: pp. 59-69.
17. Rummelhart DE, McLelland JL. Parallel distributed processing: Explorations in the microstructure of Cognition, Vol. I, Foundation, MIT Press, 1987.
18. Kohonen T. The self-organizing map. Neuro-computing.1998; 21: 1-6.
19. Carpenter GA, Grossberg S. A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine. Computer Vision, Graphics and Image Processing. 1987; vol. 37: 54-115.
20. Carpenter GA, Grossberg S. ART2: Self-Organiziation of Stable Category Recognition Codes for Analog Input Patterns. Applied Optics.1987; vol. 26 (23): 4919-4930.
21. Carpenter GA, Grossberg S. AERT 3-Hierarchical Search Using Chemical Transmitters in Self-Organizing Pattern Recognition Architecture. Neural Networks.1990; 3 (2): 129-152.
22. Carpenter GA, Grossberg S, Reynold JH. ARTMAP: Supervised Real–Time Learning and Classification of Non-Stationary Data by a Self-organizing Neural Networks. Neural Networks. 1991;4(5): 565-588.
23. Carpenter GA, Grossberg S, Rosen DB. ART-2A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition. Neural Networks.1991; 4(4): 493-504.
24. Grossberg S. Adaptive Pattern Classification and Universal Recording II: Illusions. Biological Cybernetics.1976; 3: 187-202.
25. Kohonen T. Learning Vector Quantization for Pattern Recognition. Technical Report. TKK-F-A601, Helsinki University of Technology, Finland,1986.
26. Kohonen T. Improved Versions of Learning Vector Quantization. Proceedings of the International Joint Conference on Neural Networks, San Diego.1990; CA, vol. 1: pp. 545-550.
27. Nasrabadi NM, King RA. Image Coding Using Vector Quantization: A Review. IEEE Transactions on Communications.1988; vol-36, pp. 957-971.
28. Hosmer DW, Lemeshon S.Applied Logistic Regression: New York; Wiley, 1984.