The use of feature selection based data mining methods in biomarkers identification of disease

Feature selection based data mining methods is one of the most important research directions in the fields of machine learning especially in recent years. We found that feature selection based data mining methods better suit to identifying biomarkers for disease as well as syndrome in Traditional Chinese Medicine. In this paper, we presented a novel computational strategy to select biomarkers as few as possible for disease. Firstly, we compared the three types of feature selection based data mining methods, i.e., Filter, Wrapper and Embedded methods and using 3 fold cross validation to evaluate computational performances. Alternatively, we combined independent t test and classification based data mining methods as well as backward elimination technique to select as few as possible biomarkers with best classification performances. By the novel method, we select least biomarkers for disease. And found the associated biomedical literatures support the finding. The novel method presented here provides a better insight into the pathology of a disease. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]