ArticleBMC medical informatics and decision making2024
Classification of coronary artery disease using radial artery pulse wave analysis via machine learning.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multimodal deep learning for cardiovascular disease detection using pulse wave and vocal signals: a prediction model development and validation study.BMC medical informatics and decision making · 2026Article
- Exploring the Potential of AI and Augmented Reality in Cardiovascular Disease Management: A Narrative Review.Current cardiology reviews · 2026Review
- Noninvasive Pulse Measurements for Cardiovascular Health Monitoring and Diagnoses.Advances in experimental medicine and biology · 2026Review
- Characterization of di-gital, tri-gital, and tetra-gital temporal movement of systolic blood pressure on the arterial pulse waveform of rats at different vascular stiffness.Animal models and experimental medicine · 2025Article
- Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.Balkan medical journal · 2025Article
- Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.Biomedical engineering online · 2025Article
- A heart failure classification model from radial artery pulse wave using LSTM neural networks.BMC medical informatics and decision making · 2025Article
- Association between estimated pulse wave velocity and mortality risk in patients with acute ischemic stroke.Scientific reports · 2025Article
- Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.Journal of inflammation research · 2025Article
- Fusing wrist pulse and ECG data for enhanced identification of coronary heart disease and its complications.Frontiers in physiology · 2025Article
- Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.Clinical interventions in aging · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundCoronary 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).
methodsSix 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).
resultsThe 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
conclusionRadial 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.
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