Performance improvement of atherosclerosis risk assessment based on feature interaction

Cardiovascular disease is a leading cause of mortality and premature death. Early intervention in asymptomatic individuals through risk assessment can reduce the incidence of disease. Atherosclerosis is a major cause of cardiovascular disease and early detection can effectively prevent and treat it. In this study, we used real patient data to evaluate the risk of atherosclerosis, assisting doctors in diagnosis and reducing the incidence of cardiovascular disease.

We proposed a multi-stage atherosclerosis risk assessment model that includes three main stages: (i) SMOTE and decorrelation weighting algorithm technology were added to the causal stability middle layer to address class imbalance in the dataset and reduce the impact of feature-induced dataset distribution shifts on model differences. (ii) The feature interaction layer considered possible feature interactions and classified features by different categories. By adding more effective feature information, the accuracy and generalizability of the model were improved. (iii) In the integrated model layer, we chose LightGBM as the decision tree integration model for risk assessment because it has higher accuracy and robustness compared to other machine learning algorithms.

The final model used a dataset containing 21 original features and 17 interaction features, achieving excellent performance under a 10-fold cross-validation strategy. The macro accuracy reached 93.86%, macro precision was 94.82%, macro recall was 93.52%, and macro F1 score was as high as 93.37%. These indicators demonstrate the accuracy and robustness of the model in atherosclerosis risk assessment.

The model provides strong support for the prevention and diagnosis of cardiovascular disease. Through atherosclerosis risk assessment, the model can help doctors develop personalized prevention and treatment plans, which is of great significance for the prevention and treatment of cardiovascular disease.