| Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm |
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| 作者姓名： | 颜红梅 |
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| 作者单位： | College of |
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| 基金项目： | the Natural Science Foundation of China (No. 30070211). |
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| 摘 要： | 1. Introduction Statistics has consistently shown that heart disease is one of the leading causes of death all over the world 1]. Every year, millions of people suffer from various types of heart diseases, among which coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease are the commonest. Significant life saving can be achieved if an accurate diagnosis decision, which is the prerequisite of a proper and timely treatment, ca…
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Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm |
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| YAN Hongmei,PENG Chenglin,DING Xiaojun,XIAO Shouzhong College of Bioengineering,Chongqing University,Chongqing,P.R. China Sinosoft South Information Industry Co. Ltd.,Yiyang,P.R. China.Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm[J].Journal of Chongqing University,2003,2(1). |
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| Authors: | YAN Hongmei PENG Chenglin DING Xiaojun XIAO Shouzhong College of Bioengineering Chongqing University Chongqing PR China Sinosoft South Information Industry Co Ltd Yiyang PR China |
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| Institution: | 1. College of Bioengineering, Chongqing University, Chongqing 400044, P.R. China
2. Sinosoft South Information Industry Co. Ltd., Yiyang 413000, P.R. China |
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| Abstract: | A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis. |
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| Keywords: | multilayer perceptron back propagation algorithm heart disease momentum term adaptive learning rate the forgetting mechanics conjugate gradients method |
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