Breast cancer identification based on RS and BP neural networks
Jiaxin Li, Weibing Feng · 2022
Breast cancer has become the most cancerous cancer in the world. Along with the increase in the number of confirmed breast cancer patients, the early diagnosis of breast cancer is extremely important. Machine learning methods have a wide range of applications in breast cancer tumor diagnosis, of which artificial neural networks are an effective tool for computer-assisted breast cancer diagnosis, and error backpropagation (BP) neural networks are the most widely used models. This paper provides a diagnostic decision based on the combination of rough set (RS) and BP neural network, using the rough set genetic reduction algorithm as a feature selection tool, and using the minimum number of features and the number of identifiable features in the optimal distinction matrix as the fitness function to achieve feature reduction, remove redundant attributes, further improve the diagnostic accuracy of breast cancer on the basis of a single neural network classification model, and select the five feature factors with the greatest influence on breast cancer. Experiments with breast cancer datasets in the UCI database show that this method effectively improves the accuracy of classified diagnosis compared with the original BP neural network.