Document Type : Research Paper

Authors

• Mahmoud Zahedian Nezhad ¹
• Mohammad Mehraeen ²
• Rouhollah Bagheri ³
• Seyyed Mohammad Tabatabaei ⁴

¹ Ph.D student, Department of Information Technology Management, Ferdowsi University of Mashhad, Mashhad, Iran

² Professor, Department of Information Technology Management, Ferdowsi University of Mashhad, Mashhad, Iran Corresponding Author: mehraeen@um.ac.ir

³ Associate Professor, Department of Information Technology Management, Ferdowsi University of Mashhad, Mashhad, Iran

⁴ Assistant Professor, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Cardiovascular Diseases (CVDs) represent a primary cause of global mortality. The proliferation of complex data from diagnostic tools like ECG poses significant challenges for clinicians, affecting diagnostic accuracy and delaying treatment. While Ensemble Learning (EL) offers enhanced performance by integrating multiple models, a systematic comparison of its techniques within CVD management has been limited. This study utilizes a meta-synthesis to investigate the application of EL models, often combined with Machine Learning (ML) and Deep Learning (DL). The research aims to categorize EL models in CVD management, evaluate their performance, identify their advantages and limitations, and analyze the role of feature engineering. Our findings show that EL applications are classified into four domains: prediction, diagnosis, identification, and classification. The results confirm EL models are dominant across all categories, with their effectiveness heightened when integrated with ML and DL. Notably, Random Forest (RF) and gradient boosting models like XGBoost are the most frequently implemented and highest-performing techniques, consistently yielding superior results. This study offers valuable insights for researchers and clinicians, providing a framework for applying hybrid models to achieve more precise and effective management of cardiovascular diseases.

Keywords

• Cardiovascular Diseases Management
• Meta-synthesis Approach
• Ensemble Learning (EL)
• Machine Learning (ML)
• Deep Learning (DL)

Main Subjects

• Using Artificial Intelligence inManagement