Revisiting performance metrics for prediction with rare outcomes.

ABSTRACT: Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under the receiver operating characteristic curve and other measures of accuracy are commonly reported for evaluating binary prediction problems, these metrics can be misleading. We aim to give clinical and machine learning researchers a realistic medical example of the dangers of relying on a single measure of discriminatory performance to evaluate binary prediction questions. Prediction of medical complications after surgery is a frequent but challenging task because many post-surgery outcomes are rare. We predicted post-surgery mortality among pat

SUBMITTER: Adhikari S 

PROVIDER: S-EPMC8561661 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature