A review of intelligent data analysis: Machine learning approaches for addressing class imbalance in healthcare - challenges and perspectives

Intelligent data analysis rapidly transforms healthcare care by improving patient care and predicting health outcomes through machine learning (ML) techniques. These advanced analytical methods allow intelligent healthcare systems to process large amounts of health data, improving diagnosis, treatment, and patient monitoring. The success of these systems is highly dependent on the quality and balance of the data they analyze. Class imbalance, a situation where certain classes dominate the dataset, can significantly affect the accuracy and effectiveness of ML models. In healthcare, it is not only crucial, but urgent, to accurately represent all conditions, including rare diseases, to ensure proper diagnosis and treatment. For this analysis, data was gathered from six reputable academic databases: ScienceDirect, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. This review offers a comprehensive overview of current approaches to handling class imbalance, including data preprocessing methods like oversampling, undersampling, hybrid techniques, and ensemble learning strategies such as bagging, boosting, and AdaBoost. It also addresses the limitations of these methods and the ongoing challenges in effectively managing class imbalance in healthcare data. Furthermore, the review explores innovative and promising strategies that have shown success in overcoming class imbalance, with a particular emphasis on fairness, diversity, and ethical considerations, offering a hopeful outlook for the future of healthcare data analysis. The discussion highlights how class imbalance can impact the accuracy and reliability of intelligent healthcare systems, underscoring its significance in improving patient care, healthcare delivery, and the broader medical community.