Kokori Emmanuel, Patel Ravi, Olatunji Gbolahan, Ukoaka Bonaventure Michael, Abraham Israel Charles, Ajekiigbe Victor Oluwatomiwa, Kwape Julia Mimi, Babalola Adetola Emmanuel, Udam Ntishor Gabriel, Aderinto Nicholas
Department of Medicine and Surgery, University of Ilorin, Ilorin, Nigeria.
Department of Internal Medicine, Methodist Health System Dallas, Dallas, TX, USA.
Heart Fail Rev. 2025 Mar;30(2):431-442. doi: 10.1007/s10741-024-10474-y. Epub 2024 Dec 10.
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.
心力衰竭是一种复杂且普遍存在的病症，对患者管理和生存预测具有重大影响。传统的预测模型往往因依赖预先指定的预测指标和变量独立性假设而在准确性方面存在不足。本综述旨在评估机器学习（ML）算法在预测心力衰竭生存方面的作用，将其性能与传统统计方法进行比较，并识别关键预测特征。我们对利用ML算法进行心力衰竭生存预测的研究进行了综述。数据来源于PubMed/MEDLINE、谷歌学术、ScienceDirect、Embase、DOAJ和Cochrane图书馆，涵盖截至2024年7月发表的研究。共审查了10项研究，涉及468,171例心力衰竭患者。ML算法，特别是随机森林和梯度提升方法，与传统统计模型相比表现出卓越的性能。这些算法有效地识别了关键风险因素，并将患者高精度地分层到风险类别中。值得注意的是，极限学习机（ELM）和CatBoost模型表现出卓越的预测能力，如Harrell一致性指数（C指数）和曲线下面积（AUC）等指标所示。关键预测特征包括射血分数（EF）、血清肌酐（S Cr）和血尿素氮（BUN）。ML算法通过揭示复杂模式和改善风险分层，在预测心力衰竭生存方面具有显著优势。将它们整合到临床实践中可能会带来更个性化的治疗策略并改善患者预后。然而，数据质量、模型可解释性以及整合到临床工作流程等挑战仍需解决。
