Use of machine learning to identify risk factors for coronary artery disease

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.

Fig 1. Restricted operator characteristic curve and model statistics.

The ROC Curve for the machine-learning model predicting coronary artery disease. AUROC = 0.89.

Fig 2. Overall SHAP explanations.

SHAP explanations, purple color representing higher values of the covariate while yellow representing lower values of the covariate. X-axis is the change in log-odds for CAD.

Fig 3. SHAP explanations for the top 4 covariates.

SHAP explanations, covariate value on the x-axis, change in log-odds on the y-axis, red line represents the relationship between the covariate and log-odds for CAD, each black dot represents an observation. Covariates (top left—Age, top right—Total Cholesterol, bottom left—platelets, bottom right—Close relative with a heart attack (Yes = 1, no = 2)).

Fig 4

a: Covariates of interest to evaluate sensibility of the model. SHAP explanations for the relationship between HDL-Cholesterol and odds of CAD. Covariate value on the x-axis, change in log-odds on the y-axis, red line represents the relationship between the covariate and log-odds for CAD, each black dot represents an observation. b: SHAP explanations for the relationship between Systolic blood pressure and odds of CAD. Covariate value on the x-axis, change in log-odds on the y-axis, red line represents the relationship between the covariate and log-odds for CAD, each black dot represents an observation.