The application provides a
coronary artery disease early prediction model construction method,
system, device and medium. The application synchronously collects clinical covariates and fasting
plasma lipidomics data of a subject, relies on a
quality control system to ensure that the detection data is accurate and reliable, pre-processes and standardizes lipid characteristics, constructs
unified model input variables in combination with clinical indicators, divides training sets and validation sets, screens core lipid characteristics in combination with CAD
pathological mechanisms, eliminates redundant variables, and strengthens the relevance of characteristics and coronary
artery lesions. A multivariate
logistic regression model is constructed using the
training set, and the validation set is used for evaluation to ensure the model discrimination performance and generalization ability. The key parameters of the model are solidified and a parameter table is formed, and a standardized CAD risk
estimation model is established. In clinical application, only a single
blood test combined with basic demographic information can quickly quantify the
early disease risk, realize efficient screening and accurate
risk stratification, and provide an objective basis for early warning and individualized
clinical diagnosis and treatment of
coronary artery disease.