Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/7/1663 |
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| author | Cătălina-Lucia Cocianu Cristian Răzvan Uscatu Konstantinos Kofidis Sorin Muraru Alin Gabriel Văduva |
| author_facet | Cătălina-Lucia Cocianu Cristian Răzvan Uscatu Konstantinos Kofidis Sorin Muraru Alin Gabriel Văduva |
| author_sort | Cătălina-Lucia Cocianu |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages. |
| first_indexed | 2024-03-11T05:38:58Z |
| format | Article |
| id | doaj.art-8f8d8d5eaf604e96b1c5ac9c06e8ad94 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T05:38:58Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-8f8d8d5eaf604e96b1c5ac9c06e8ad942023-11-17T16:33:49ZengMDPI AGElectronics2079-92922023-03-01127166310.3390/electronics12071663Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case StudyCătălina-Lucia Cocianu0Cristian Răzvan Uscatu1Konstantinos Kofidis2Sorin Muraru3Alin Gabriel Văduva4Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, RomaniaCardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.https://www.mdpi.com/2079-9292/12/7/1663evolution strategiestree-structured Parzen estimator (TPE)LSTM neural networksSVM classificationdata preprocessingrandom forest |
| spellingShingle | Cătălina-Lucia Cocianu Cristian Răzvan Uscatu Konstantinos Kofidis Sorin Muraru Alin Gabriel Văduva Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study Electronics evolution strategies tree-structured Parzen estimator (TPE) LSTM neural networks SVM classification data preprocessing random forest |
| title | Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study |
| title_full | Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study |
| title_fullStr | Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study |
| title_full_unstemmed | Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study |
| title_short | Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study |
| title_sort | classical evolutionary and deep learning approaches of automated heart disease prediction a case study |
| topic | evolution strategies tree-structured Parzen estimator (TPE) LSTM neural networks SVM classification data preprocessing random forest |
| url | https://www.mdpi.com/2079-9292/12/7/1663 |
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