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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41102/1/23977-50936-1-PB.pdf http://umpir.ump.edu.my/id/eprint/41102/ http://doi.org/10.11591/ijai.v13.i2.pp2433-2440 http://doi.org/10.11591/ijai.v13.i2.pp2433-2440 |
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| Summary: | 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. |
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