Machine learning for the prediction of atherosclerotic cardiovascular disease during 3-year follow up in Chinese type 2 diabetes mellitus patients

Abstract

Aims/introduction: Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3-year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients.

Materials and methods: Clinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model.

Results: All five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors.

Conclusions: The prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions.

Keywords: Atherosclerotic cardiovascular disease; Machine learning; Type 2 diabetes mellitus.

Figure 1

Framework for predicting atherosclerotic cardiovascular disease (ASCVD) risk in Chinese type 2 diabetes mellitus patients. AdaBoost, adaptive boosting; GBDT, gradient boosting decision tree; LR, logistic regression; RF, random forest; SHAP, SHapley Additive exPlanations; SVM, support vector machine; T2DM, type 2 diabetes mellitus.

Figure 2

Analysis overview for identifying the best‐performing atherosclerotic cardiovascular disease risk prediction model. 3BExt, 3BExt database.

Figure 3

Kernel density plot for (a) fasting plasma glucose (FPG), (b) glycosylated hemoglobin (HbA1c), (c) low‐density lipoprotein (LDL), (d) total cholesterol (TCHO), (e) triglycerides (TG) and (f) high‐density lipoprotein (HDL). ASCVD, atherosclerotic cardiovascular disease.

Figure 4

Receiver operating curves for the prediction of atherosclerotic cardiovascular disease using different machine learning models in the testing set. AdaBoost, adaptive boosting; GBDT, gradient boosting decision tree; LR, logistic regression; RF, random forest; SVM, support vector machine.

Figure 5

Receiver operating curves for the prediction of atherosclerotic cardiovascular disease using different machine learning models in the external validation set. AdaBoost, adaptive boosting; GBDT, gradient boosting decision tree; LR, logistic regression; RF, random forest; SVM, support vector machine.

Figure 6

Importance matrix plot of the random forest model. BMI, body mass index; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; HDL, high‐density lipoprotein cholesterol; LDL, low‐density lipoprotein cholesterol; TCHO, total cholesterol; TG, triglycerides.

Figure 7

SHapley Additive explanation (SHAP) summary plot of the 10 most predictive features of the RF model. HbA1c, glycosylated hemoglobin; TCHO, total cholesterol; TG, triglycerides.

Figure 8

SHapley Additive exPlanation (SHAP) dependence plot of the random forest model for (a) age, (b) diabetes duration, (c) triglycerides (TG) and (d) diagnostic age.