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一种基于虚拟编码和弹性网络惩罚多项逻辑回归的红斑鳞屑性疾病诊断新方法

[A New Method for Diagnosing Erythemato-squamous Diseases Based on Virtual Coding and Multinomial Logistic Regression Penalized via Elastic Net].

作者信息

Wang Jinjia, Li Hui

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Aug;32(4):757-62.

Abstract

Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34% +/- 0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.

摘要

红斑鳞屑性疾病是六种常见皮肤病的统称,其鉴别诊断是皮肤科的一个难题。本文提出了一种基于定性变量虚拟编码和弹性网惩罚多项逻辑回归的新方法。考虑到变量的属性,应用虚拟编码有助于避免直接计算名义值的不合理性。进而使用弹性网惩罚多项逻辑回归模型来拟合疾病特征与分类之间的相关性。最后,通过坐标下降法获得参数估计值。该方法在实验中采用10折交叉验证,准确率达到98.34%±0.0027%。与其他方法的结果相比,我们的方法获得了相当的准确率,但步骤更简单,稳定性更高。

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