一种改进的字典学习方法在医疗疾病分析中的应用

doi:10.16180/j.cnki.issn1007-7820.2019.02.010

Abstract

Abstract:

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

LUO Chong,WU Chunxue. An Improved Dictionary Learning Method for Medical Disease Analysis[J].Electronic Science and Technology, 2019, 32(2): 47-50.

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References 20

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Algorithm	Accuracy/%	Sensitivity/%	Specificity/%
SVM^[20]	62.73	33.33	87.50
SRC^[10]	72.73	66.67	75.00
FDDL^[15]	76.32	64.87	80.41
Improved	89.73	87.59	92.56

An Improved Dictionary Learning Method for Medical Disease Analysis

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