Visualising and quantifying the usefulness of new predictors stratified by outcome class: The U-smile method.

ABSTRACT: Binary classification methods encompass various algorithms to categorize data points into two distinct classes. Binary prediction, in contrast, estimates the likelihood of a binary event occurring. We introduce a novel graphical and quantitative approach, the U-smile method, for assessing prediction improvement stratified by binary outcome class. The U-smile method utilizes a smile-like plot and novel coefficients to measure the relative and absolute change in prediction compared with the reference method. The likelihood-ratio test was used to assess the significance of the change in prediction. Logistic regression models using the Heart Disease dataset and generated random variables were employed to validate the U-smile method. The receiver operating characteristic (ROC) curve was used to

SUBMITTER: Kubiak KB 

PROVIDER: S-EPMC11104627 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature