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.

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

“…high expectations in revolutionary changes in health care in CAD patients [26][27][28]. The deep learning and machine learning algorithm could achieve more accurate results and outperform statistical methods.…”

Diabetes is associated with increased risk of death in COVID‐19 hospitalizations in Mexico 2020: A retrospective cohort study

“…While the existing body of research consistently associates diabetes with severe COVID‐19 outcomes, certain gaps and limitations in previous studies need to be addressed. Many studies have not adequately accounted for confounding factors such as the type of diabetes, disease duration, glycemic control, presence of diabetic complications, and comorbidities like obesity, hypertension, and cardiovascular diseases 25 . These factors contribute to the heterogeneity of the diabetic population and may impact the association between diabetes and COVID‐19 morbidity.…”

“…Many studies have not adequately accounted for confounding factors such as the type of diabetes, disease duration, glycemic control, presence of diabetic complications, and comorbidities like obesity, hypertension, and cardiovascular diseases. 25 These factors contribute to the heterogeneity of the diabetic population and may impact the association between diabetes and COVID‐19 morbidity. Moreover, studies conducted in different countries have reported varying mortality rates among COVID‐19 patients with diabetes.…”