Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center
- سال انتشار: 1401
- محل انتشار: مجله داده های بزرگ و چشم انداز محاسباتی، دوره: 2، شماره: 1
- کد COI اختصاصی: JR_BDCV-2-1_003
- زبان مقاله: انگلیسی
- تعداد مشاهده: 163
نویسندگان
Department of Computer Engineering, Technical and Vocational University, Iran.
Department of Computer, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
Department of Computer, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
چکیده
Coronary artery heart failure is the leading cause of mortality among other cardiac diseases. In most of the cases, angiography is a reliable method for the diagnosis and treatment of cardiovascular diseases. However, it is a costly approach associated with various complications. The significant increase in the prevalence of cardiovascular diseases and the subsequent complications and treatment costs have urged researchers to plan for the better examination, prevention, early detection, and effective treatment of these conditions. The present study aimed to determine the patterns of cardiovascular diseases using integrated classification techniques for analyzing the data of internal medicine patients who are at the risk of heart failure with ۴۵۱ samples and ۱۳ characteristics. Selecting characteristics and evaluating the influential factors are essential to the development of classifiers and increasing their accuracy. Therefore, we investigated the influential factors of the Gini index. In the classification phase, basic techniques were used, including a decision tree, a neural network, and different cumulative techniques such as gradient boosting, random forest, and the novel deep learning method. A comparison revealed that deep learning with the accuracy of ۹۵.۳۳%, disease class accuracy of ۹۵.۷۷%, and health class accuracy of ۹۴.۷۴% could enhance the presentation and results of the neural network. Out findings confirmed that cumulative methods and selecting influential factors are essential to increasing the accuracy of the diagnostic systems for heart failure. Furthermore, the reported practical tree rules emphasized the use of analytical methods to extract knowledge.کلیدواژه ها
Data Analysis, Diagnosis of Heart Failure, Cumulative Algorithms, Deep Learningاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.