Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.
and stability
cardiovascular disease risk
hybrid deep learning
machine learning feature extraction
performance evaluation
reliability
scientific validation
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
28 Aug 2024
28 Aug 2024
Historique:
received:
10
07
2024
revised:
12
08
2024
accepted:
26
08
2024
medline:
14
9
2024
pubmed:
14
9
2024
entrez:
14
9
2024
Statut:
epublish
Résumé
The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0 The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the The hypothesis for AtheroEdge™ 3.0
Sections du résumé
BACKGROUND
BACKGROUND
The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0
METHODOLOGY
METHODS
500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0
RESULTS
RESULTS
The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the
CONCLUSIONS
CONCLUSIONS
The hypothesis for AtheroEdge™ 3.0
Identifiants
pubmed: 39272680
pii: diagnostics14171894
doi: 10.3390/diagnostics14171894
pii:
doi:
Types de publication
Journal Article
Langues
eng