Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center
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 inc...
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REA Press
2022-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_145116_58a98e3ca831bfd43039e38a097173c0.pdf |
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| author | Amir Hossein Hariri Esmaeil Bagheri Sayyed Mohammad Reza Davoodi |
| author_facet | Amir Hossein Hariri Esmaeil Bagheri Sayyed Mohammad Reza Davoodi |
| author_sort | Amir Hossein Hariri |
| collection | DOAJ |
| description | 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 451 samples and 13 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 95.33%, disease class accuracy of 95.77%, and health class accuracy of 94.74% 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. |
| format | Article |
| id | doaj-art-f9aa8423747a4fe39072b0c1ca44b4ba |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2022-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-f9aa8423747a4fe39072b0c1ca44b4ba2025-01-30T12:21:39ZengREA PressBig Data and Computing Visions2783-49562821-014X2022-03-0121183010.22105/bdcv.2022.325710.1043145116Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart centerAmir Hossein Hariri0Esmaeil Bagheri1Sayyed Mohammad Reza Davoodi2Department 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.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 451 samples and 13 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 95.33%, disease class accuracy of 95.77%, and health class accuracy of 94.74% 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.https://www.bidacv.com/article_145116_58a98e3ca831bfd43039e38a097173c0.pdfdata analysisdiagnosis of heart failurecumulative algorithmsdeep learning |
| spellingShingle | Amir Hossein Hariri Esmaeil Bagheri Sayyed Mohammad Reza Davoodi Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center Big Data and Computing Visions data analysis diagnosis of heart failure cumulative algorithms deep learning |
| title | Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center |
| title_full | Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center |
| title_fullStr | Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center |
| title_full_unstemmed | Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center |
| title_short | Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center |
| title_sort | presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms a case study of tehran heart center |
| topic | data analysis diagnosis of heart failure cumulative algorithms deep learning |
| url | https://www.bidacv.com/article_145116_58a98e3ca831bfd43039e38a097173c0.pdf |
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