Cong Jinyu, Wei Benzheng, He Yunlong, Yin Yilong, Zheng Yuanjie
School of Information Science and Engineering, Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan 250358, China.
College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250014, China.
Comput Math Methods Med. 2017;2017:4896386. doi: 10.1155/2017/4896386. Epub 2017 Jun 27.
Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator presents a new way to choose the base classifier for ensemble learning.
乳腺癌一直是威胁女性生命的主要疾病之一。乳腺癌的早期检测和诊断对于降低乳腺癌死亡率起着重要作用。在本文中，我们提出了一种将KNN、支持向量机（SVM）和朴素贝叶斯相结合的选择性集成方法，用于结合超声图像和乳腺X线摄影图像来诊断乳腺癌。我们的实验结果表明，这种选择性分类方法的准确率为88.73%，灵敏度为97.06%，对乳腺癌诊断是有效的。并且指标 为集成学习选择基础分类器提供了一种新方法。
