مطالعات مدیریت کسب و کار هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری رشته مدیریت فناوری اطلاعات، گروه مدیریت، دانشگاه فردوسی مشهد، مشهد، ایران

2 استاد مدیریت فناوری اطلاعات،گروه مدیریت، دانشگاه فردوسی مشهد، مشهد، ایران نویسنده مسئول: mehraeen@um.ac.ir

3 دانشیار مدیریت فناوری اطلاعات،گروه مدیریت، دانشگاه فردوسی مشهد، مشهد، ایران

4 استادیار انفورماتیک پزشکی،گروه انفورماتیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد مشهد، ایران

چکیده

بیماری‌های قلبی-عروقی (CVDs) یکی از علل اصلی مرگ‌ومیر در سراسر جهان محسوب می‌شوند. افزایش داده‌های پیچیده حاصل از ابزارهای تشخیصی مانند الکتروکاردیوگرام (ECG)، چالش‌های قابل توجهی را برای پزشکان ایجاد کرده که بر دقت تشخیص و سرعت درمان تأثیر می‌گذارد. یادگیری گروهی (EL) با ترکیب مدل‌های مختلف، عملکرد بهتری را در مدیریت CVD ها ارائه می‌دهد، اما تحقیقات محدودی به صورت سیستماتیک تکنیک‌های مختلف آن را مقایسه کرده‌اند. این پژوهش با استفاده از رویکرد فراترکیب، به بررسی کاربرد مدل‌های EL در ترکیب با مدل‌های یادگیری ماشین (ML) و یادگیری عمیق (DL) می‌پردازد. هدف این مطالعه، دسته‌بندی مدل‌های EL در مدیریت CVD ها، ارزیابی عملکرد و کارایی آن‌ها در هر دسته، شناسایی مزایا و محدودیت‌ها و تحلیل نقش مهندسی ویژگی است. یافته‌های این فراترکیب نشان می‌دهد که کاربرد مدل‌های یادگیری در مدیریت CVD ها به چهار حوزه‌ی اصلی تقسیم می‌شود: پیش‌بینی، تشخیص، شناسایی و طبقه‌بندی. نتایج تأیید می‌کند که مدل‌های EL در تمام این چهار حوزه غالب هستند و کارایی آن‌ها با ادغام با تکنیک‌های ML و DL به طور قابل توجهی افزایش می‌یابد. در میان رویکردهای مختلف، مدل‌های جنگل تصادفی (RF) و الگوریتم‌های تقویت گرادیان مانند XGBoost، بیشترین فراوانی استفاده را داشته و به عنوان کارآمدترین و دقیق‌ترین مدل‌ها شناخته می‌شوند. این پژوهش با ارائه یک نمای کلی ساختاریافته، بینش‌های ارزشمندی را برای محققان و متخصصان بالینی فراهم می‌کند و چارچوبی برای به‌کارگیری مدل‌های ترکیبی جهت دستیابی به مدیریت دقیق‌تر و مؤثرتر بیماری‌های قلبی-عروقی ارائه می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comprehensive Evaluation of Ensemble Learning (EL) Models in Cardiovascular Diseases Management: A Meta-Synthesis Approach

نویسندگان [English]

  • 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

چکیده [English]

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.

کلیدواژه‌ها [English]

  • Cardiovascular Diseases Management
  • Meta-synthesis Approach
  • Ensemble Learning (EL)
  • Machine Learning (ML)
  • Deep Learning (DL)
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    استناد به این مقاله: زاهدیان نژاد، محمود.، مهرآیین، محمد.، باقری، روح‌الله.، طباطبایی، سید محمد. (1405). ارزیابی جامع مدل‌های یادگیری گروهی (EL) در مدیریت بیماری‌های قلبی عروقی: رویکردی فراترکیب، مطالعات مدیریت کسب وکار هوشمند، 15(55)، 39-126. DOI: 10.22054/ims.2025.86650.2638

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