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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: Ku Muhammad Naim, Ku Khalif, Noryanti, Muhammad, Mohd Khairul Bazli, Mohd Aziz, Mohammad Isa, Irawan, Mohammad Iqbal, ., Muhammad Nanda, Setiawan
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) 2024
Subjects:
QA Mathematics
QA76 Computer software
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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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
QA76 Computer software
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
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.
format Article
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
title Advancing machine learning for identifying cardiovascular disease via granular computing
title_short 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_sort advancing machine learning for identifying cardiovascular disease via granular computing
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2024
url 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
_version_ 1822924303008530432
score 13.235362

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