Breast Cancer Classification Based on Ensemble Learning
DENG Zhuo1,2，SU Binghua1,2，ZHANG Kai2
1. Key Laboratory of Photoelectric Imaging and System, Ministry of Education, Zhuhai College of Beijing Institute of Technology, Zhuhai
Guangdong 519088, China|2. Beijing Institute of Technology, Beijing 100081, China
Abstract：Objective Due to the low classification ability of traditional machine learning algorithm, it is not enough to assist clinical diagnosis. This project combines the ensemble learning with strong classification function for medical diagnosis to improve the
diagnosis accuracy and recall rate. Methods In this project, the random forest algorithm and Xgboost algorithm of ensemble learning were applied to improve the accuracy and recall rate of the model. Cross validation and grid search were used to improve the model
fitting ability. Results By comparing random forest model, Xgboost model and decision tree model of traditional machine learning, we can see that ensemble learning has greatly improved the accuracy and recall rate of breast cancer diagnosis. The accuracy rate
increased from 0.92 to 0.96, and the recall rate increased from 0.90 to 0.97 and 0.99. Conclusion It has practical research significance to combine the ensemble learning algorithm with the actual medical diagnosis technology. We can further combine the two fields to
improve the effectiveness of medical diagnosis.
邓卓1,2，苏秉华1,2，张凯2. 基于集成学习的乳腺癌分类研究[J]. 中国医疗设备, 2020, 35(12): 59-62.
DENG Zhuo1,2，SU Binghua1,2，ZHANG Kai2. Breast Cancer Classification Based on Ensemble Learning. China Medical Devices, 2020, 35(12): 59-62.
