期刊
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
卷 2022, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2022/6715406
关键词
-
Breast cancer is the second leading cause of death in developed and developing nations, characterized by gene mutation, constant pain, size fluctuations, color, and breast skin texture. By applying various classification algorithms on the WBCD dataset, it was found that random forest achieved a classification accuracy of 96.24%, outperforming other classifiers.
Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据