Back to Search Start Over

Using Anti-Malondialdehyde Modified Peptide Autoantibodies to Import Machine Learning for Predicting Coronary Artery Stenosis in Taiwanese Patients with Coronary Artery Disease

Authors :
Yu-Cheng Hsu
I-Jung Tsai
Hung Hsu
Po-Wen Hsu
Ming-Hui Cheng
Ying-Li Huang
Jin-Hua Chen
Meng-Huan Lei
Ching-Yu Lin
Source :
Diagnostics, Vol 11, Iss 6, p 961 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Machine learning (ML) algorithms have been applied to predicting coronary artery disease (CAD). Our purpose was to utilize autoantibody isotypes against four different unmodified and malondialdehyde (MDA)-modified peptides among Taiwanese with CAD and healthy controls (HCs) for CAD prediction. In this study, levels of MDA, MDA-modified protein (MDA-protein) adducts, and autoantibody isotypes against unmodified peptides and MDA-modified peptides were measured with enzyme-linked immunosorbent assay (ELISA). To improve the performance of ML, we used decision tree (DT), random forest (RF), and support vector machine (SVM) coupled with five-fold cross validation and parameters optimization. Levels of plasma MDA and MDA-protein adducts were higher in CAD patients than in HCs. IgM anti-IGKC76–99 MDA and IgM anti-A1AT284–298 MDA decreased the most in patients with CAD compared to HCs. In the experimental results of CAD prediction, the decision tree classifier achieved an area under the curve (AUC) of 0.81; the random forest classifier achieved an AUC of 0.94; the support vector machine achieved an AUC of 0.65 for differentiating between CAD patients with stenosis rates of 70% and HCs. In this study, we demonstrated that autoantibody isotypes imported into machine learning algorithms can lead to accurate models for clinical use.

Details

Language :
English
ISSN :
20754418
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.3ca673cc96bd4189ad93c45e53786a17
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics11060961