Hybrid intelligent system for stratifying the risk of coronary heart disease

The article proposes a hybrid intelligent system for stratifying the risk of coronary heart disease (CHD). It includes ensemble forecasting, natural language processing, and explainable AI technologies. It is demonstrated that the integration of structured clinical features and unstructured text data improves the accuracy of CHD risk assessment compared to the traditional SCORE2 scales. On test datasets, high values of AUC-ROC and F1-score were achieved, while the interpretability of the predictions is ensured by constructing waterfall plots that reflect the contribution of key risk factors. The developed software package will be useful in cardiology for supporting clinical decision-making.