Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model

BACKGROUND: To establish a prediction model for cardiovascular diseases (CVD) in the general population based on random forests. METHODS: A retrospective study involving 498 subjects was conducted in Xi'an Medical University between 2011 and 2018. The random forest algorithm was used to screen out the variables that greatly affected the CVD prediction and to establish a prediction model. The important variables were included in the multifactorial logistic regression analysis. The area under the curve (AUC) was compared between logistic regression model and random forest model. RESULTS: The random forest model revealed the variables, including the age, body mass index (BMI), fasting blood glucose (FBG), diastolic blood pressure (DBP), triglyceride (TG), systolic blood pressure (SBP), total cholesterol (TC), waist circumference, and high-density lipoprotein-cholesterol (HDL-C), were more significant for CVD prediction; the AUC was 0.802 in CVD prediction. Multifactorial logistic regression analysis indicated that the risk factors for CVD included the age [odds ratio (OR): 1.14, 95% confidence intervals (CI): 1.10-1.17, P < .001], BMI (OR: 1.13, 95% CI: 1.06-1.20, P < .001), TG (OR: 1.11, 95% CI: 1.02-1.22, P = .023), and DBP (OR: 1.04, 95% CI: 1.02-1.06, P = .001); the AUC was 0.843 in CVD prediction. The established logistic regression prediction model was Logit P = Log[P/(1 - P)] = -11.47 + 0.13 × age + 0.12 × BMI + 0.11 × TG + 0.04 × DBP; P = 1/[1 + exp(-Logit P)]. People were prone to develop CVD at the time of P > .51. CONCLUSIONS: A prediction model for CVD is developed in the general population based on random forests, which provides a simple tool for the early prediction of CVD.