Identifying Metabolite Biomarkers in Unstable Angina In-Patients by Feature Selection Based Data Mining Methods

Unstable angina (UA) is a most dangerous type of Coronary Heart Disease (CHD) that causing more and more mortality and morbidity world wide. Identification of biomarkers for UA in the level of metabolomics is a better avenue to understand the inner mechanism of it. We carried out clinical epidemiology to collect plasmas of UA in-patients and controls. Metabolomics data are obtained by gas chromatography techniques. We presented a novel computational strategy to select biomarkers as few as possible for UA in the data. We combined independent t test and classification based data mining methods as well as backward elimination technique to select as few as possible metabolite biomarkers with best classification performances. By the novel method, we select five metabolites for UA. The associated biomedical literatures support the finding. The novel method presented here provides a better insight into the pathology of a disease. Feature selection based data mining methods better suit to identifying biomarkers for UA