ArticlePloS one2023
Use of machine learning to identify risk factors for coronary artery disease.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Development and validation of the C-reactive protein-triglyceride-glucose index for predicting short- and long-term mortality in critically ill patients with coronary artery disease: a multicenter cohort study.Frontiers in cardiovascular medicine · 2026Article
- Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- NSUN2 knockdown ameliorates hepatic glucose and lipid metabolism disorders in type 2 diabetes mellitus through the Inhibition of ACSL6 m5C methylation.Lipids in health and disease · 2025Article
- Predicting depression risk in COPD patients: a model based on NHANES 2007-2012 data.BMC public health · 2025Article
- Deep learning health space model for ordered responses.BMC medical informatics and decision making · 2025Article
- The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.Biological trace element research · 2025Article
- Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.BMC geriatrics · 2025Article
- Association of dietary health indices with frailty.BMC public health · 2025Article
- Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma.Scientific reports · 2025Article
- Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.Scientific reports · 2025Article
- The associations of physical activity patterns and the triglyceride-glucose index in US adults: a secondary data analysis of NHANES (2007-2018).Scientific reports · 2025Article
- Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?BMC endocrine disorders · 2025Article
- Identify the underlying true model from other models for clinical practice using model performance measures.BMC medical research methodology · 2025Article
- Inverse associations of the lifestyle critical 9 with cardiorenal syndrome: the mediating role of the dietary inflammatory index.Frontiers in nutrition · 2025Article
- GAN-Augmented Naïve Bayes for identifying high-risk coronary artery disease patients using CT angiography data.Scientific reports · 2024Article
- Article
- Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease.Frontiers in cardiovascular medicine · 2024Article
- Use machine learning models to identify and assess risk factors for coronary artery disease.PloS one · 2024Article
- Diabetes is associated with increased risk of death in COVID-19 hospitalizations in Mexico 2020: A retrospective cohort study.Health science reports · 2023Article
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2 authors.
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Abstract
Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.
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