一種改進(jìn)的SVM算法在乳腺癌診斷方面的應(yīng)用
發(fā)布時(shí)間：2018-09-19 12:26
【摘要】：針對(duì)計(jì)算機(jī)輔助診斷(CAD)技術(shù)在乳腺癌疾病診斷準(zhǔn)確率的優(yōu)化問題,提出了一種基于隨機(jī)森林模型下Gini指標(biāo)特征加權(quán)的支持向量機(jī)方法(RFG-SVM)。該方法利用了隨機(jī)森林模型下的Gini指數(shù)衡量各個(gè)特征對(duì)分類結(jié)果的重要性,構(gòu)造具有加權(quán)特征向量核函數(shù)的支持向量機(jī),并在乳腺癌疾病診斷方面加以應(yīng)用。經(jīng)理論分析和實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證,相比于傳統(tǒng)的支持向量機(jī)(SVM),該方法提升了分類預(yù)測(cè)的性能,其結(jié)果與最新的方法相比也具有一定的競(jìng)爭(zhēng)力,而且在醫(yī)療診斷應(yīng)用方面更具優(yōu)勢(shì)。
[Abstract]:In order to optimize the diagnostic accuracy of computer-aided diagnosis (CAD) in breast cancer, a new support vector machine (RFG-SVM) method based on Gini index weighted under stochastic forest model is proposed. This method uses the Gini index under the stochastic forest model to measure the importance of each feature to the classification result, constructs the support vector machine with weighted eigenvector kernel function, and applies it to the diagnosis of breast cancer disease. Through theoretical analysis and experimental data verification, compared with the traditional support vector machine (SVM), this method improves the performance of classification and prediction, and the results are competitive compared with the latest methods, and it has more advantages in medical diagnosis application.
【作者單位】： 蘭州交通大學(xué)電子與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61163010) 甘肅省自然科學(xué)基金(1308RJZA111) 蘭州市科技計(jì)劃(2015-2-99)
【分類號(hào)】：R737.9;TP391.7
[Abstract]:In order to optimize the diagnostic accuracy of computer-aided diagnosis (CAD) in breast cancer, a new support vector machine (RFG-SVM) method based on Gini index weighted under stochastic forest model is proposed. This method uses the Gini index under the stochastic forest model to measure the importance of each feature to the classification result, constructs the support vector machine with weighted eigenvector kernel function, and applies it to the diagnosis of breast cancer disease. Through theoretical analysis and experimental data verification, compared with the traditional support vector machine (SVM), this method improves the performance of classification and prediction, and the results are competitive compared with the latest methods, and it has more advantages in medical diagnosis application.
【作者單位】： 蘭州交通大學(xué)電子與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61163010) 甘肅省自然科學(xué)基金(1308RJZA111) 蘭州市科技計(jì)劃(2015-2-99)
【分類號(hào)】：R737.9;TP391.7
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1 吳辰文;李長(zhǎng)生;王偉;梁靖涵;閆光輝;;一種改進(jìn)的SVM算法在乳腺癌診斷方面的應(yīng)用[J];計(jì)算機(jī)工程與科學(xué);2017年03期
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