發(fā)布時(shí)間：2018-09-05 13:03

【摘要】：冠心病的早期無創(chuàng)性診斷一直是醫(yī)療診斷領(lǐng)域的研究熱點(diǎn),為了提高冠心病診斷的準(zhǔn)確率和診斷效率,提出了一種新穎的局部Fisher判別分析(LFDA)特征提取方法和集成核極限學(xué)習(xí)機(jī)(KELM)相結(jié)合的冠心病診斷模型(LFDA-EKELM)。首先使用LFDA方法剔除不相關(guān)特征和冗余特征,找出對(duì)分類結(jié)果貢獻(xiàn)度較高的特征子集,產(chǎn)生不同的訓(xùn)練集以訓(xùn)練粒子群優(yōu)化的KELM分類器PSO-KELM;基于旋轉(zhuǎn)森林(RF)構(gòu)建集成分類器,實(shí)現(xiàn)冠心病的智能診斷。實(shí)驗(yàn)結(jié)果表明,與基于ELM、SVM和BPNN方法相比,該方法有效提高了冠心病診斷準(zhǔn)確率、提升了診斷效率,且分類結(jié)果高于已有方法和相似方法,是一種有效冠心病診斷模型。
[Abstract]:The early noninvasive diagnosis of coronary heart disease (CHD) has been a hot topic in the field of medical diagnosis. In order to improve the accuracy and efficiency of coronary heart disease diagnosis, A novel (LFDA) feature extraction method based on local Fisher discriminant analysis and an integrated kernel limit learning machine (KELM) model for coronary heart disease diagnosis (LFDA-EKELM) are proposed. Firstly, the LFDA method is used to eliminate the irrelevant and redundant features, and the feature subsets with high contribution to the classification results are found, and different training sets are generated to train the particle swarm optimization KELM classifier PSO-KELM; based on the rotating forest (RF) to construct the integrated classifier. To realize intelligent diagnosis of coronary heart disease. The experimental results show that compared with the ELM,SVM and BPNN methods, this method can effectively improve the diagnostic accuracy of coronary heart disease and improve the diagnostic efficiency, and the classification results are higher than the existing methods and similar methods, so it is an effective diagnosis model of coronary heart disease.
【作者單位】： 深圳信息職業(yè)技術(shù)學(xué)院數(shù)字媒體學(xué)院;
【基金】：國(guó)家自然科學(xué)青年基金資助項(xiàng)目(61303113) 廣東省自然科學(xué)基金資助項(xiàng)目(2016A0303100072) 深圳市科技計(jì)劃資助項(xiàng)目(GJHZ20150316112246318)
【分類號(hào)】：R541.4;TP181

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1 邵耕;應(yīng)用無創(chuàng)性診斷方法的一些問題[J];中華心血管病雜志;1994年03期

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