Key result
XGBoost machine learning predicts CAD with ~0.89 AUROC using age, platelets, family history, and cholesterol.
Why the study?
Coronary artery disease is the leading cause of death globally, and identifying risk factors using machine learning methods requires assessment.
Can an XGBoost machine learning model accurately predict coronary artery disease and identify key risk factors using demographic, laboratory, physical exam, and lifestyle covariates?
Cross-Sectional (n=7,929)
Can an XGBoost machine learning model accurately predict coronary artery disease and identify key risk factors using demographic, laboratory, physical exam, and lifestyle covariates?
A machine learning model using routine clinical, demographic, and lifestyle data can accurately predict coronary artery disease and identify key risk factors such as age and platelet count.
Answers are grounded in this paper and cite their sources.
Machine learning CAD prediction with routine data remains hypothesis-generating; prospective validation required before any practice change.
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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Huang et al. (2023) conducted a cross-sectional in Coronary artery disease (n=7,929). Demographic, laboratory, physical exam, and lifestyle covariates was evaluated on Prediction of coronary artery disease (AUROC 0.89). An XGBoost machine learning model effectively predicted coronary artery disease with an AUROC of 0.89, identifying age, platelet count, family history of heart disease, and total cholesterol as the top risk factors.
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