INTEGRATED APPROACH TO ASSESSING CHD SEVERITY: MODEL, DIGITAL TOOL AND TRAINING

Abstract

This article presents the results of an integrated study aimed at refining a mathematical model for assessing the severity of coronary artery disease (CAD), developing an electronic tool, and implementing it in medical education. The proposed approach utilizes logistic regression, gradient boosting, and Bayesian analytics to build a predictive model based on clinical, laboratory, and angiographic data. The developed software demonstrated high accuracy (AUC = 0.89) and usability in clinical settings. The educational methodology designed for integrating this digital tool into the learning process significantly improved academic outcomes and digital competencies of medical students. This work exemplifies an interdisciplinary synthesis of science, practice, and education in the era of digital healthcare transformation.