Optimal quantile level selection for disease classification and biomarker discovery with application to electrocardiogram data

Classification with a large number of predictors and biomarker discovery become increasingly important in biological and medical research. This paper focuses on performing classification of cardiovascular diseases based on electrocardiogram analysis which deals with many variables and a lot of measurements within variables. We propose an optimal quantile level selection procedure to reduce dimension by characterizing distributions with quantiles and combine with classification tools to produce sensible classification and biomarker discovery results. Simulation and an intensive study of a real data set are performed to illustrate the performance of the proposed method.