Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis

Frontiers in Cardiovascular Medicine

article

Zhoujian Sun¹  Wei Dong³  Hanrui Shi²  Hong Ma⁴  Lechao Cheng¹  Zhengxing Huang² 

[1] Zhejiang Lab;Zhejiang University;Department of Cardiology, The First Medical Center of Chinese PLA General Hospital;Department of Cardiology, The Second Affiliated Hospital of School of Medicine, Zhejiang University

关键词: heart failure;    prediction model;    machine learning;    systematic review;    statistical model;   

DOI  :  10.3389/fcvm.2022.812276

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Objective To compare the performance, clinical feasibility, and reliability of statistical and machine learning (ML) models in predicting heart failure (HF) events. Background Although ML models have been proposed to revolutionize medicine, their promise in predicting HF events has not been investigated in detail. Methods A systematic search was performed on Medline, Web of Science, and IEEE Xplore for studies published between January 1, 2011 to July 14, 2021 that developed or validated at least one statistical or ML model that could predict all-cause mortality or all-cause readmission of HF patients. Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias, and random effect model was used to evaluate the pooled c-statistics of included models. Result Two-hundred and two statistical model studies and 78 ML model studies were included from the retrieved papers. The pooled c-index of statistical models in predicting all-cause mortality, ML models in predicting all-cause mortality, statistical models in predicting all-cause readmission, ML models in predicting all-cause readmission were 0.733 (95% confidence interval 0.724–0.742), 0.777 (0.752–0.803), 0.678 (0.651–0.706), and 0.660 (0.633–0.686), respectively, indicating that ML models did not show consistent superiority compared to statistical models. The head-to-head comparison revealed similar results. Meanwhile, the immoderate use of predictors limited the feasibility of ML models. The risk of bias analysis indicated that ML models' technical pitfalls were more serious than statistical models'. Furthermore, the efficacy of ML models among different HF subgroups is still unclear. Conclusions ML models did not achieve a significant advantage in predicting events, and their clinical feasibility and reliability were worse.

【 授权许可】

CC BY