Meta‐Heuristic Optimization for the Multi‐Classification of Chronic Disease: A Review With Machine Learning Perspectives

Abstract

Chronic diseases (CDs) present a global health challenge due to their complex, overlapping symptoms and the limitations of traditional diagnostic methods. Artificial intelligence (AI)‐based techniques, particularly Machine Learning (ML) and Meta‐Heuristic Optimization (MHO) algorithms, have emerged as powerful tools for addressing these challenges. This review examines ML and MHO‐based approaches for the multi‐classification of CDs, highlighting how MHO enhances ML frameworks by addressing key limitations such as class imbalance and suboptimal feature selection. Despite these advancements, MHO‐based methods face challenges, including computational complexity and algorithmic biases, which require further research. By critically analyzing existing studies and identifying gaps, this paper provides a foundation for developing more robust and efficient diagnostic models for CDs.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction