基于核主成分分析支持向量机的乳腺癌辅助诊断

Auxiliary Diagnosis of Breast Cancer Based on Kernel Principal Component Analysis Support Vector Machine

摘要: 本文利用核主成分分析法对乳腺癌的影响因子进行特征提取，以获取的主成分作为支持向量机的特征向量建立支持向量机模型，其中模型参数分别通过粒子群算法和遗传算法进行选择优化，分别构建出KPCA-PSO-SVM模型和KPCA-GA-SVM模型，对乳腺肿块是否为恶性进行二分类。实验结果显示：KPCA-PSO-SVM模型和KPCA-GA-SVM模型相比PSO-SVM模型和GA-SVM模型在分类准确率方面和运行速度方面均有所提高，表明核主成分分析支持向量机可以用于乳腺癌疾病的辅助诊断，可以为医疗机构对乳腺癌疾病的诊断提供有力的决策支持。

Abstract: Kernel principal component analysis (KPCA) was used to extract the feature factors of breast cancer. The principal components were obtained as support vector machine (SVM) feature vector to establish support vector machine model. The model parameters were selected and optimized re-spectively by PSO and GA. KPCA-PSO-SVM model and KPCA-GA-SVM model were constructed to classify the breast masses as malignant. The experimental results show that the KPCA-PSO-SVM model and KPCA-GA-SVM model both improve the classification accuracy and the operation speed compared with the PSO-SVM model and GA-SVM model, which shows that the principal component analysis support vector machine can be used in the auxiliary diagnosis of breast cancer and can provide strong decision-making support for the diagnosis of breast cancer in medical institutions.