A Comprehensive Review of Machine Learning Techniques for Predicting Patient Outcomes

Abstract

In the realm of healthcare, predicting patient outcomes is of paramount importance for effective treatment planning and resource allocation. Machine learning (ML) techniques have emerged as powerful tools for analyzing patient data and making accurate predictions. This paper presents a comprehensive review of various ML techniques employed in predicting patient outcomes. We delve into the intricacies of these methods, highlighting their strengths, weaknesses, and applications in healthcare. The review encompasses a wide range of ML algorithms, including but not limited to, logistic regression, random forests, support vector machines, neural networks, and ensemble methods. Additionally, we discuss the challenges associated with implementing these techniques in real-world healthcare settings and propose future research directions to enhance their effectiveness.