Breast Cancer Diagnosis and Survival Prediction Using JNN

Download Edit this record How to cite View on PhilPapers
Abstract
Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental results on Haberman’s Breast Cancer Survival dataset show the superiority of proposed method by reaching 88.24 % in terms of accuracy. The results are the best reported ones obtained from Artificial Neural Network using JNN environment without any preprocessing of the dataset.
Categories
No categories specified
(categorize this paper)
PhilPapers/Archive ID
SHABCD-3
Upload history
Archival date: 2020-10-29
View other versions
Added to PP index
2020-10-29

Total views
318 ( #21,142 of 64,194 )

Recent downloads (6 months)
192 ( #2,615 of 64,194 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.