Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

Abstract

AbstractIntroductionHeart disease remains a leading cause of global mortality. While acute myocardial infarction (colloquially: heart attack), has multiple proximate causes, proximate etiology cannot be determined by a blood-based diagnostic test. We enrolled a suitable patient cohort and conducted an untargeted quantification of plasma metabolites by mass spectrometry for developing a test that can differentiate between thrombotic MI, non-thrombotic MI, and stable disease. A significant challenge in developing such a diagnostic test is solving the NP-hard problem of feature selection for constructing an optimal statistical classifier.ObjectiveWe employed a Wisdom of Artificial Crowds (WoAC) strategy for solving the feature selection problem and evaluated the accuracy and parsimony of downstream classifiers in comparison with embedded feature selection via the Lasso and Elastic Net.Materials and MethodsArtificial Crowd Wisdom was generated via aggregation of the best solutions from independent and diverse genetic algorithm populations that were initialized with bootstrapping and a random subspaces constraint.Results / ConclusionsWoAC feature selection performed favorably compared to Lasso and Elastic Net solutions. The classifier constructed following WoAC feature selection had a cross-validation estimated misclassification rate of 2.6% as compared to 26.3% via the Lasso and 18.5% via an Elastic Net. The classifier warrants further evaluation as a diagnostic test in an independent cohort.