Classification of coronary artery disease using radial artery pulse wave analysis via machine learning.
Coronary artery disease
Extra Trees Classifier
Machine learning
Radial pulse wave
Journal
BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682
Informations de publication
Date de publication:
16 Sep 2024
16 Sep 2024
Historique:
received:
02
07
2024
accepted:
05
09
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
16
9
2024
Statut:
epublish
Résumé
Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. There is an urgent need to develop non-invasive, rapid, cost-effective, and reliable techniques for the early detection of CAD using machine learning (ML). Six hundred eight participants were divided into three groups: healthy, hypertensive, and CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost). The Extra Trees classifier demonstrated the best classification performance. After tunning, the results performance evaluation on test set are: 0.8579 accuracy, 0.9361 AUC, 0.8561 recall, 0.8581 precision, 0.8571 F1 score, 0.7859 kappa coefficient, and 0.7867 MCC. The top 10 feature importances of ET model are w/t Radial artery pulse wave can be used to identify healthy, hypertensive and CAD participants by using Extra Trees Classifier. This method provides a potential pathway to recognize CAD patients by using a simple, non-invasive, and cost-effective technique.
Sections du résumé
BACKGROUND
BACKGROUND
Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. There is an urgent need to develop non-invasive, rapid, cost-effective, and reliable techniques for the early detection of CAD using machine learning (ML).
METHODS
METHODS
Six hundred eight participants were divided into three groups: healthy, hypertensive, and CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost).
RESULTS
RESULTS
The Extra Trees classifier demonstrated the best classification performance. After tunning, the results performance evaluation on test set are: 0.8579 accuracy, 0.9361 AUC, 0.8561 recall, 0.8581 precision, 0.8571 F1 score, 0.7859 kappa coefficient, and 0.7867 MCC. The top 10 feature importances of ET model are w/t
CONCLUSION
CONCLUSIONS
Radial artery pulse wave can be used to identify healthy, hypertensive and CAD participants by using Extra Trees Classifier. This method provides a potential pathway to recognize CAD patients by using a simple, non-invasive, and cost-effective technique.
Identifiants
pubmed: 39285363
doi: 10.1186/s12911-024-02666-1
pii: 10.1186/s12911-024-02666-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
256Subventions
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : National Natural Science Foundation of China
ID : 81673880
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Key Laboratory of Health Identification and Assessment Project
ID : 21DZ2271000
Organisme : Shanghai Science and Technology Innovation Action Plan Technical Standards Program
ID : 21DZ2203100
Informations de copyright
© 2024. The Author(s).
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