Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratific...

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Vydáno v:Journal of proteome research Ročník 22; číslo 3; s. 758
Hlavní autoři: Liu, Wuping, Zhang, Lirong, Bao, Lijun, Shen, Guiping, Feng, Jianghua
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 03.03.2023
Témata:
Humans
Machine Learning
Magnetic Resonance Imaging
Myocardial Infarction - diagnosis
Prognosis
ROC Curve
clinical classification
machine learning
metabolomics
nuclear magnetic resonance
acute myocardial infarction
ISSN:1535-3907, 1535-3907
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Shrnutí:The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, 1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.
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ISSN:1535-3907
1535-3907
DOI:10.1021/acs.jproteome.2c00488