Group Lasso Penalized Classifier for Diagnosis of Diseases with Categorical Data.
- Author:
Jinjia WANG
;
Fang XUE
- Publication Type:Journal Article
- MeSH:
Algorithms;
Computational Biology;
methods;
Humans;
Reproducibility of Results;
Skin Diseases;
classification;
diagnosis
- From:
Journal of Biomedical Engineering
2015;32(5):965-969
- CountryChina
- Language:Chinese
-
Abstract:
Six kinds of erythemato-squamous diseases have been common skin diseases, but the diagnosis of them has always been a problem. The quantitative data processing method is not suitable for erythemato-squamous data because they are categorical qualitative data. This paper proposed a new method based on group lasso penalized classification for the feature selection and classification for erythemato-squamous data with categorical qualitative data. The first categorical data of 33 dimensions were changed by the virtual code, and then 34th dimension age data were discretized and changed by the virtual code. Then the encoded data were grouped according to class group and variable group. Lastly Group Lasso penalized classification was executed. The classified accuracy of 10-fold cross validation was 98.88% ± 0.002 3%. Compared with those of other method in the literature, this new method is simpler, and better for effect and efficiency, and has stronger interpretability and stronger stability.
