Author:
Salmi Mabrouka,Atif Dalia,Oliva Diego,Abraham Ajith,Ventura Sebastian
Abstract
AbstractMachine learning and medical diagnostic studies often struggle with the issue of class imbalance in medical datasets, complicating accurate disease prediction and undermining diagnostic tools. Despite ongoing research efforts, specific characteristics of medical data frequently remain overlooked. This article comprehensively reviews advances in addressing imbalanced medical datasets over the past decade, offering a novel classification of approaches into preprocessing, learning levels, and combined techniques. We present a detailed evaluation of the medical datasets and metrics used, synthesizing the outcomes of previous research to reflect on the effectiveness of the methodologies despite methodological constraints. Our review identifies key research trends and offers speculative insights and research trajectories to enhance diagnostic performance. Additionally, we establish a consensus on best practices to mitigate persistent methodological issues, assisting the development of generalizable, reliable, and consistent results in medical diagnostics.
Funder
Spanish Ministry of Science and Innovation and the European Fund for Region Development
Universidad de Córdoba
Publisher
Springer Science and Business Media LLC