A multilayer perceptron-based medical decision support system for heart disease diagnosis

  • Authors:

  • Hongmei Yan;Yingtao Jiang;Jun Zheng;Chenglin Peng;Qinghui Li

  • Affiliations:

  • Life Science and Technology Institute, University of Electronic Science and Technology of China, Chengdu, Peoples's Republic of China 610054;Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA;Department of Computer Science, Queens College-City University of New York, Flushing, NY 11367, USA;Bioengineering Institute, Chongqing University, Chongqing, Peoples's Republic of China 400044;Southwest Hospital, Chongqing, Peoples's Republic of China 400038

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2006

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Abstract

The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (90%) and comparably small intervals (