Advancing machine learning for identifying cardiovascular disease via granular computing
Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computi...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science (IAES)
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41102/ http://umpir.ump.edu.my/id/eprint/41102/1/23977-50936-1-PB.pdf |
| _version_ | 1848826237397499904 |
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| author | Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Mohammad Isa, Irawan Mohammad Iqbal, . Muhammad Nanda, Setiawan |
| author_facet | Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Mohammad Isa, Irawan Mohammad Iqbal, . Muhammad Nanda, Setiawan |
| author_sort | Ku Muhammad Naim, Ku Khalif |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection. |
| first_indexed | 2025-11-15T03:41:38Z |
| format | Article |
| id | ump-41102 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:41:38Z |
| publishDate | 2024 |
| publisher | Institute of Advanced Engineering and Science (IAES) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-411022024-05-06T02:23:54Z http://umpir.ump.edu.my/id/eprint/41102/ Advancing machine learning for identifying cardiovascular disease via granular computing Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Mohammad Isa, Irawan Mohammad Iqbal, . Muhammad Nanda, Setiawan QA Mathematics QA76 Computer software Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection. Institute of Advanced Engineering and Science (IAES) 2024-06 Article PeerReviewed pdf en cc_by_nc_sa_4 http://umpir.ump.edu.my/id/eprint/41102/1/23977-50936-1-PB.pdf Ku Muhammad Naim, Ku Khalif and Noryanti, Muhammad and Mohd Khairul Bazli, Mohd Aziz and Mohammad Isa, Irawan and Mohammad Iqbal, . and Muhammad Nanda, Setiawan (2024) Advancing machine learning for identifying cardiovascular disease via granular computing. IAES International Journal of Artificial Intelligence (IJ-AI), 13 (2). pp. 2433-2440. ISSN 2252-8938. (Published) http://doi.org/10.11591/ijai.v13.i2.pp2433-2440 http://doi.org/10.11591/ijai.v13.i2.pp2433-2440 |
| spellingShingle | QA Mathematics QA76 Computer software Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Mohammad Isa, Irawan Mohammad Iqbal, . Muhammad Nanda, Setiawan Advancing machine learning for identifying cardiovascular disease via granular computing |
| title | Advancing machine learning for identifying cardiovascular disease via granular computing |
| title_full | Advancing machine learning for identifying cardiovascular disease via granular computing |
| title_fullStr | Advancing machine learning for identifying cardiovascular disease via granular computing |
| title_full_unstemmed | Advancing machine learning for identifying cardiovascular disease via granular computing |
| title_short | Advancing machine learning for identifying cardiovascular disease via granular computing |
| title_sort | advancing machine learning for identifying cardiovascular disease via granular computing |
| topic | QA Mathematics QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/41102/ http://umpir.ump.edu.my/id/eprint/41102/ http://umpir.ump.edu.my/id/eprint/41102/ http://umpir.ump.edu.my/id/eprint/41102/1/23977-50936-1-PB.pdf |