A Novel Classification Method for Analysis of Multi-stage Diseases via Mass Spectrometric Data

Multi-category classification is one of the challenging issues in medical data analysis. We propose a new bi- classification algorithm for the multi-class classification, which is comprised of two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). After fea- ture reduction in both schemes, each corresponding classi- fication strategy is performed. For a test sample, two class labels that are predicted in both schemes are compared. If two class labels are the same, we assign the test sample to an identical label; otherwise, only for samples belonging to different classes predicted from two schemes, a retraining method is employed. Our scheme is applied to the analysis of a MALDI-TOF data set which consists of hepatocellular carcinoma (HCC) patients, cirrhosis patients and healthy individuals. To validate the performance of our proposed algorithm, experiments were performed in comparison with other classification methods.