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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Cardiovascular; Fuzzy numbers; Granular computing; Machine learning; Z-numbers |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Center for Mathematical Science |
| Depositing User: | Dr. Ku Muhammad Na'im Ku Khalif |
| Date Deposited: | 06 May 2024 02:23 |
| Last Modified: | 06 May 2024 02:23 |
| URI: | http://umpir.ump.edu.my/id/eprint/41102 |
| Download Statistic: | View Download Statistics |
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