Review of Machine Learning Techniques for Heart Failure Patient Classification and Prediction in Healthcare

Abstract

This review explores the application of machine learning (ML) techniques for the classification and prediction of heart failure patients in healthcare. Heart failure is a critical health issue affecting a significant population globally, making accurate prediction and classification vital for effective treatment and management. ML algorithms offer promising solutions by leveraging large datasets to identify patterns and predict outcomes based on patient data. The review begins by discussing the importance of accurate classification in optimizing treatment strategies and improving patient outcomes. It then examines various ML techniques employed in heart failure prediction, including supervised learning algorithms like logistic regression, decision trees, and ensemble methods such as random forests and gradient boosting. Additionally, the review discusses the use of unsupervised learning techniques like clustering for patient stratification based on clinical characteristics. the review highlights challenges in the application of ML, such as data quality issues, interpretability of models, and scalability in clinical settings. It concludes with an outlook on future research directions aimed at enhancing the performance and applicability of ML techniques in heart failure prediction and management.