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

• Published in: PloS one Vol. 19; no. 5; p. e0303276
• Main Authors: Kubiak, Katarzyna B, Więckowska, Barbara, Jodłowska-Siewert, Elżbieta, Guzik, Przemysław
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.05.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Cardiovascular diseases; Classification; Comparative analysis; Data points; Evaluation; Forecasting; Heart Diseases; Humans; Likelihood Functions; Logistic Models; Machine learning; Medicine and Health Sciences; Methodology; Methods; Physical Sciences; Predictions; Random variables; Regression analysis; Regression models; Research and Analysis Methods; ROC Curve; Treatment outcome; Taiwan; Poland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303276

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 compare the results of the U-smile method. The likelihood-ratio test demonstrated that the proposed coefficients consistently generated smile-shaped U-smile plots for the most informative predictors. The U-smile plot proved more effective than the ROC curve in comparing the effects of adding new predictors to the reference method. It effectively highlighted differences in model performance for both non-events and events. Visual analysis of the U-smile plots provided an immediate impression of the usefulness of different predictors at a glance. The U-smile method can guide the selection of the most valuable predictors. It can also be helpful in applications beyond prediction.