Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction

BMC Medical Informatics and Decision Making

Research

Yiting Wang¹  Jiancheng Xu¹  Xuewen Li¹  Qi Zhou²  Chengming Shang³  Changyan Xu⁴ 

[1] Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, China;Department of Pediatrics, First Hospital of Jilin University, 1Xinmin Street, 130021, Changchun, Jilin, China;Information center, First Hospital of Jilin University, Changchun, China;Medical Department, First Hospital of Jilin University, Changchun, China;

关键词: Acute myocardial infarction;    Heart failure;    Machine learning;    Extreme gradient boosting;    Model;   

DOI  :  10.1186/s12911-023-02240-1

 received in 2022-12-29, accepted in 2023-07-13,  发布年份 2023

来源: Springer

【 摘 要 】

AimsHeart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms.MethodsCohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively.ResultsThe best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits.ConclusionsThis study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 1