Abstract
Predicting disease outcomes and patient readmission rates is a critical area of research in healthcare, aiming to improve patient care, reduce hospital burdens, and optimize resource allocation. This survey paper provides a comprehensive analysis of the various techniques employed for disease outcome prediction and readmission rate estimation. The study reviews a range of machine learning, deep learning and statistical methods, highlighting their strengths, limitations, and applications in diverse clinical scenarios. Key methods discussed include regression models, decision trees, support vector machines, ensemble learning and neural networks, alongside emerging approaches such as explainable AI and federated learning. The paper also examines data preprocessing techniques, feature selection methods, and evaluation metrics, emphasizing their role in enhancing predictive accuracy and reliability.