Machine learning in heart failure diagnosis, prediction, and prognosis: review

Abstract

Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure. This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of heart failure (HF) in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of heart failure with preserved ejection fraction (HFpEF) and the identification of high-risk patients with heart failure with reduced ejection fraction (HFrEF). The prediction of mortality in different heart failure populations using different ML approaches is explored, encompassing patients in the intensive care unit (ICU), and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in heart failure patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of heart failure. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of machine learning diagnosis, prediction, and prognosis of heart failure across different subtypes and patient populations.