Review of Machine Learning Techniques For Class Imbalance Medical Data Set

Abstract

Data imbalance threatens a medical dataset where the dominant class is typically viewed as unfavorable. In contrast, the minority class is t supposed to be the positive one, affecting the performance of the machine learning prediction. This aims to examine how resampling strategies in Machine Learning (ML) have recently been used in medical data sets. Many researchers used the preprocessing stage's data-level approach to resample the imbalanced medical data. Thirty-two sources were reviewed in which data level techniques of balancing the imbalanced data were applied to medical datasets spanning 2015 to 2022, with oversampling methods outperforming the under-sampling methods.