计算机工程与科学2017，Vol.39Issue(3)：562-566,5.DOI:10.3969/j.issn.1007-130X.2017.03.023
一种改进的SVM算法在乳腺癌诊断方面的应用
Application of an improved support vector machine algorithm in the diagnosis of breast cancer
摘要
Abstract
To optimize the accuracy of computer aided diagnosis (CAD) technology in the diagnosis of breast cancer,we propose a new support vector machine algorithm based on the feature weighting of Gini index under the random forest model (RFG-SVM).The algorithm uses the Gini index under the random forest model to measure the impact of each feature on the classification results,and to build a support vector machine with the weighted feature vector kernel function,which is then applied to the diagnosis of breast cancer.Theoretical analysis and experimental data tests show that the proposed algorithm has higher classification accuracy than the traditional SVM and is more competitive than the stateof-the-art methods in medical diagnostics.关键词
支持向量机/特征加权/随机森林/计算机辅助诊断Key words
SVM/feature weighting/random forest/computer-aided diagnosis (CAD)分类
信息技术与安全科学引用本文复制引用
吴辰文,李长生,王伟,梁靖涵,闫光辉..一种改进的SVM算法在乳腺癌诊断方面的应用[J].计算机工程与科学,2017,39(3):562-566,5.基金项目
国家自然科学基金(61163010) （61163010）
甘肃省自然科学基金(1308RJZA111) （1308RJZA111）
兰州市科技计划(2015-2-99) （2015-2-99）
