Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (2): 47-50.doi: 10.16180/j.cnki.issn1007-7820.2019.02.010

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An Improved Dictionary Learning Method for Medical Disease Analysis

LUO Chong,WU Chunxue   

  1. School of Optical Electrical & Computer Engineering,University of Shanghai for Science & Technology,Shanghai 200093,China
  • Received:2018-01-18 Online:2019-02-15 Published:2019-01-02
  • Supported by:
    Shanghai Science and Technology Innovation Action Plan Project(16111107502);Shanghai Science and Technology Innovation Action Plan Project(17511107203)


This paper proposed the weighted mechanism to combine the sample with the dictionary atom based on the traditional dictionary learning algorithm, which ignored the relationship between the sample and the dictionary atom. At the same time, the l2 norm regularization constraint was adopted to avoid over fitting on coding coefficients. The paper used the leave one out cross validation to compare the accuracy, sensitivity and mean error rate of the algorithm in the depression data sets. The results showed that the improved method had a good effect on the medical disease classification. The average classification accuracy was improved by 21.87%, and the sensitivity and mean error rate also displayed good performance.

Key words: medical big data, dictionary learning, sparse representation, disease classification, weighted mechanism, paradigm constraint

CLC Number: 

  • TP301.6