Predicting COPD status with a random generalized linear model

Abstract

Sample classification, especially disease status prediction, is an important area of investigation for gene expression studies. Many machine learning methods have been developed to tackle this problem. To evaluate different prediction methods, the IMPROVER Challenge made several data sets available. Here we focus on one sub-challenge: chronic obstructive pulmonary disease (COPD). We outlined critical preprocessing steps to make training and test data comparable. We compared our recently introduced random generalized linear model (RGLM) predictor with Leo Breiman’s random forest (RF) predictor on the COPD data set. We discussed potential reasons for the superior performance of the RGLM predictor in this sub-challenge. Interestingly, we found that although several genes were highly predictive of COPD status, none were necessary to achieve accurate prediction when demographic features smoking status and age were used. In conclusion, RGLM achieved superior predictive accuracy for predicting COPD status with smoking status and age as mandatory features. Future cohort studies could evaluate whether the resulting predictor has clinical utility.