Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches
Cardiovascular diseases (CVDs) are characteristics that affect both the heart and the blood vessels. This disease is the main factor contributing to the greatest number of deaths globally. In the present global context, it is very difficult to detect cardiovascular diseases by early-stage symptoms....
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| Ձևաչափ: | Հոդված |
| Լեզու: | English English |
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Penerbit Akademia Baru
2024
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| Առցանց հասանելիություն: | http://umpir.ump.edu.my/id/eprint/39151/1/Semi-supervised%20learning_%20Assisted%20cardiovascular%20disease%20forecasting.pdf http://umpir.ump.edu.my/id/eprint/39151/7/Semi-Supervised%20Learning-%20Assisted%20Cardiovascular%20Disease.pdf |
| _version_ | 1825815245044580352 |
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| author | Tusher, Ekramul Haque Mohd Arfian, Ismail Khan, Ferose Anis Farihan, Mat Raffei Md Akbar, Jalal Uddin |
| author_facet | Tusher, Ekramul Haque Mohd Arfian, Ismail Khan, Ferose Anis Farihan, Mat Raffei Md Akbar, Jalal Uddin |
| author_sort | Tusher, Ekramul Haque |
| collection | UMP |
| description | Cardiovascular diseases (CVDs) are characteristics that affect both the heart and the blood vessels. This disease is the main factor contributing to the greatest number of deaths globally. In the present global context, it is very difficult to detect cardiovascular diseases by early-stage symptoms. If this isn't diagnosed early, it could lead to death. In order to improve the accuracy of the CVD prediction system, a wide variety of supervised and unsupervised learning approaches from the fields of machine learning were used. Only labeled data is used in supervised learning systems to create a classification model but acquiring sufficient amounts of labeled data takes time and typically requires the cooperation of field experts. However, unlabeled samples are readily available in a variety of real-world situations. More effectively than any other machine learning approach, semi-supervised learning (SSL) addresses this problem by integrating quantities of labeled and unlabeled data to improve the classification model. In this work, we propose semi-supervised learning approaches based on self-learning with Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RB). According to the comparison's findings, SVM has a high classification accuracy rate of 94.68%, a recall rate of 94.41%, a sensitivity rate of 94.49%, a F1 score rate of 92.99%, a precision rate of 91.59% a low, a balanced accuracy rate 94%, a G-mean rate of 94.45 and low Error-rate 5.32%. The model may be used to forecast cardiovascular disorders in the medical profession. |
| first_indexed | 2024-12-09T02:29:40Z |
| format | Article |
| id | UMPir39151 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-12-09T02:29:40Z |
| publishDate | 2024 |
| publisher | Penerbit Akademia Baru |
| record_format | dspace |
| spelling | UMPir391512024-11-04T07:55:50Z http://umpir.ump.edu.my/id/eprint/39151/ Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches Tusher, Ekramul Haque Mohd Arfian, Ismail Khan, Ferose Anis Farihan, Mat Raffei Md Akbar, Jalal Uddin QA75 Electronic computers. Computer science Cardiovascular diseases (CVDs) are characteristics that affect both the heart and the blood vessels. This disease is the main factor contributing to the greatest number of deaths globally. In the present global context, it is very difficult to detect cardiovascular diseases by early-stage symptoms. If this isn't diagnosed early, it could lead to death. In order to improve the accuracy of the CVD prediction system, a wide variety of supervised and unsupervised learning approaches from the fields of machine learning were used. Only labeled data is used in supervised learning systems to create a classification model but acquiring sufficient amounts of labeled data takes time and typically requires the cooperation of field experts. However, unlabeled samples are readily available in a variety of real-world situations. More effectively than any other machine learning approach, semi-supervised learning (SSL) addresses this problem by integrating quantities of labeled and unlabeled data to improve the classification model. In this work, we propose semi-supervised learning approaches based on self-learning with Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RB). According to the comparison's findings, SVM has a high classification accuracy rate of 94.68%, a recall rate of 94.41%, a sensitivity rate of 94.49%, a F1 score rate of 92.99%, a precision rate of 91.59% a low, a balanced accuracy rate 94%, a G-mean rate of 94.45 and low Error-rate 5.32%. The model may be used to forecast cardiovascular disorders in the medical profession. Penerbit Akademia Baru 2024-10-08 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/39151/1/Semi-supervised%20learning_%20Assisted%20cardiovascular%20disease%20forecasting.pdf pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/39151/7/Semi-Supervised%20Learning-%20Assisted%20Cardiovascular%20Disease.pdf Tusher, Ekramul Haque and Mohd Arfian, Ismail and Khan, Ferose and Anis Farihan, Mat Raffei and Md Akbar, Jalal Uddin (2024) Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches. Journal of Advanced Research in Applied Sciences and Engineering Technology, 56 (1). pp. 136-150. ISSN 2462-1943. (In Press / Online First) (In Press / Online First) https://doi.org/10.37934/araset.56.1.136150 https://doi.org/10.37934/araset.56.1.136150 |
| spellingShingle | QA75 Electronic computers. Computer science Tusher, Ekramul Haque Mohd Arfian, Ismail Khan, Ferose Anis Farihan, Mat Raffei Md Akbar, Jalal Uddin Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title | Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title_full | Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title_fullStr | Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title_full_unstemmed | Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title_short | Semi-supervised learning: Assisted cardiovascular disease forecasting using self-learning approaches |
| title_sort | semi supervised learning assisted cardiovascular disease forecasting using self learning approaches |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/39151/1/Semi-supervised%20learning_%20Assisted%20cardiovascular%20disease%20forecasting.pdf http://umpir.ump.edu.my/id/eprint/39151/7/Semi-Supervised%20Learning-%20Assisted%20Cardiovascular%20Disease.pdf |
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