一种改进的SVM算法在乳腺癌诊断方面的应用

针对计算机辅助诊断(CAD)技术在乳腺癌疾病诊断准确率的优化问题,提出了一种基于随机森林模型下Gini指标特征加权的支持向量机方法(RFG-SVM)。该方法利用了随机森林模型下的Gini指数衡量各个特征对分类结果的重要性,构造具有加权特征向量核函数的支持向量机,并在乳腺癌疾病诊断方面加以应用。经理论分析和实验数据验证,相比于传统的支持向量机(SVM),该方法提升了分类预测的性能,其结果与最新的方法相比也具有一定的竞争力,而且在医疗诊断应用方面更具优势。

Application of an improved support vector machine algorithm in the diagnosis of breast cancer

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 state-of-the-art methods in medical diagnostics.