Predictive Models for Coronary Heart Disease Prognosis using Ensemble Learning

Аннотация

This thesis investigates the application of ensemble learning techniques in developing predictive models for coronary heart disease prognosis, aiming to enhance diagnostic capabilities and improve patient outcomes in cardiovascular medicine. By leveraging advanced computational methods and machine learning algorithms, the study focuses on automating the detection of myocardial infarction and heart conduction disorders using a deep learning model trained on ECG signals from a diverse dataset. The research methodology involves a systematic review of highly relevant papers, exclusion criteria to ensure the specificity of the study, and a search process in reputable academic libraries. Through a comparative analysis of selected papers and an in-depth exploration of machine learning approaches, the thesis aims to contribute to the advancement of predictive modeling techniques in cardiology. The findings of this research have the potential to significantly impact the field of cardiovascular care by providing more accurate prognostic tools for coronary heart disease management.