Machine learning in predicting heart failure survival: a review of current models and future prospects

Heart failure is a complex and prevalent condition with significant implications for patient management and survival prediction. Traditional predictive models often fall short in accuracy due to their reliance on pre-specified predictors and assumptions of variable independence. This review aims to assess the role of machine learning (ML) algorithms in predicting heart failure survival, comparing their performance with traditional statistical methods and identifying key predictive features. We conducted a review of studies utilizing ML algorithms for heart failure survival prediction. Data were sourced from PubMed/MEDLINE, Google Scholar, ScienceDirect, Embase, DOAJ, and the Cochrane Library, covering studies published until July 2024. A total of 10 studies were reviewed, encompassing 468,171 patients with heart failure. ML algorithms, particularly random forests and gradient boosting methods, demonstrated superior performance compared to traditional statistical models. These algorithms effectively identified key risk factors and stratified patients into risk categories with high accuracy. Notably, extreme learning machine (ELM) and CatBoost models showed exceptional predictive capabilities, as indicated by metrics such as Harrell's concordance index (C-index) and area under the curve (AUC). Key predictive features included ejection fraction (EF), serum creatinine (S Cr), and blood urea nitrogen (BUN). ML algorithms offer significant advantages in predicting heart failure survival by uncovering complex patterns and improving risk stratification. Their integration into clinical practice could lead to more personalized treatment strategies and enhanced patient outcomes. However, challenges such as data quality, model interpretability, and integration into clinical workflows need to be addressed.