本文關(guān)鍵詞：SVM在冠心病分類預測中的應(yīng)用研究，由筆耕文化傳播整理發(fā)布。

SVM在冠心病分類預測中的應(yīng)用研究

Study on Application of SVM in Prediction of Coronary Heart Disease

[1] [2] [3]

Zhu Yue Wu Jianghua Fang Ying （School of Bioscience ~ Bioengineering, South China University of Technology, Ouangzhou 510006, China）

華南理工大學生物科學與工程學院,廣州510006

文章摘要：本文基于體檢獲得的血壓、血脂、尿糖和尿酸等數(shù)據(jù)指標，應(yīng)用支持向量機（SVM）對南方人群冠心病患者和非冠心病患者進行分類研究，為進一步的預防和治療提供指導。首先選取徑向基核函數(shù)、線性核函數(shù)和多項式核函數(shù)，構(gòu)造了SVM分類器，再采用粒子群優(yōu)化（PSO）算法SVM懲罰參數(shù)C和核參數(shù)o，，最后進行冠心病的診斷和預測。通過與反向傳播模型的人工神經(jīng)網(wǎng)絡(luò)、線性判別分析法、I，ogistic回歸分析及優(yōu)化前的SVM進行比較，我們的計算結(jié)果顯示優(yōu)化后的RBF—SVM的總體分類效果要優(yōu)于其他數(shù)據(jù)挖掘算法，其分類準確率、敏感性和特異性分別高達94．51％、92．31％及96．67％。研究表明SVM在心血管疾病的預測和輔助診斷中有很大的應(yīng)用潛力。

Abstr：Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine （SVM） was applied to identify coronary heart disease （CHD） in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter ~ were optimized by particle swarm optimization （PSO） and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation （BP） model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

文章關(guān)鍵詞：

Keyword：:Support vector machine （SVM） Coronary heart disease （CHD） Prediction Particle swarm optimization （PSO）

課題項目：國家自然科學基金資助項目（10972081,11072080）;廣東省自然科學基金資助項目（S2011010005451）

作者信息：會員可見

  本文關(guān)鍵詞：SVM在冠心病分類預測中的應(yīng)用研究，由筆耕文化傳播整理發(fā)布。