Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm

Authors:
Hongmei Yan;Jun Zheng;Yingtao Jiang;Chenglin Peng;Shouzhong Xiao
Affiliations:
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;Department of Computer Science, Queens College, The City University of New York, Flushing, NY 11367, USA;Department of Electrical & Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA;Bioengineering Institute, Chongqing University, Chongqing 400044, PR China;Bioengineering Institute, Chongqing University, Chongqing 400044, PR China
Venue:
Applied Soft Computing
Year:
2008

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Abstract

In clinic, normally a lot of diagnostic features are recorded from a patient for a certain disease. It will be beneficial for the prompt and correct diagnosis of the disease by selecting the important and relevant features and discarding those irrelevant and redundant ones. In this paper, a real-coded genetic algorithm (GA)-based system is proposed to select the critical clinical features essential to the heart diseases diagnosis. The heart disease database used in this study includes 352 cases, and 40 diagnostic features were recorded for each case. Using the proposed genetic algorithm, 24 critical features have been identified, and their corresponding diagnosis weights for each heart disease of interest have been determined. The critical diagnostic features and their clinic meanings are in sound agreement with those used by the physicians in making their clinic decisions.