“Usually, researchers pick things they suspect would be risk factors for heart failure — smoking, for example — and then look at smokers versus nonsmokers,” said the senior author, Dr. David P. Kao, an assistant professor of medicine at the University of Colorado [School of Medicine]. “But machine learning identifies variables that are predictive of either increased or decreased risk, but that you haven’t necessarily thought of.”