Class imbalance in gradient boosting classification algorithms: Application to experimental stroke data. (March 2021)

• Record Type: Journal Article
• Authors: Lyashevska, Olga; Malone, Fiona; MacCarthy, Eugene; Fiehler, Jens; Buhk, Jan-Hendrik; Morris, Liam
• Is Part Of: Statistical methods in medical research. Volume 30:Number 3(2021)
• Page Start: 916
• Page End: 925
• Publication Date: 2021-03
• Subjects: Imbalanced data -- gradient boosting -- classification algorithm -- trees -- stroke -- oversampling; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220980484
• Languages: English
• ISSNs: 0962-2802

Abstract

Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Imbalanced data hinder the performance of conventional classification methods which aim to improve the overall accuracy of the model without accounting for uneven distribution of the classes. To rectify this, the data can be resampled by oversampling the positive (minority) class until the classes are approximately equally represented. After that, a prediction model such as gradient boosting algorithm can be fitted with greater confidence. This classification method allows for non-linear relationships and deep interactive effects while focusing on difficult areas by iterative shifting towards problematic observations. In this study, we demonstrate application of these methods to medical data and develop a practical framework for evaluation of features contributing into the probability of stroke.