The Journal of Science and Engineering Applications (JSEA)

Abstract

Machine learning (ML) has become a crucial tool in analyzing big data within the healthcare domain, offering predictive capabilities, personalized treatments, and enhanced decision-making processes. This review systematically examines the latest ML techniques applied to big data analysis in healthcare, including supervised and unsupervised learning, deep learning, and ensemble methods. We analyze the advantages and limitations of these approaches and their effectiveness in improving patient outcomes, diagnosing diseases, and optimizing resource allocation. Furthermore, we highlight key challenges such as data privacy, model interpretability, and computational efficiency. This paper provides a comparative assessment of ML-based healthcare solutions, offering insights into future research directions and best practices for integrating ML in real-world medical applications.