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Cardiovascular disease detection from high utility rare rule mining

We propose a method to search rare cardiovascular disease symptom rules from historical health examination records according to its hazard ratio utility and further detect the disease given new medical record data. Further, we aim to assist both medical experts and patients by alerting the current s...

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Main Authors:	Iqbal, Mohammad, Setiawan, Muhammad Nanda, Isa Irawan, Mohammad Isa, Ku Muhammad Naim, Ku Khalif, Noryanti, Muhammad, Mohd Khairul Bazli, Mohd Aziz
Format:	Article
Language:	English English
Published:	Elsevier B.V. 2022
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/40182/1/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare.pdf http://umpir.ump.edu.my/id/eprint/40182/2/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare%20rule%20mining_ABS.pdf http://umpir.ump.edu.my/id/eprint/40182/ https://doi.org/10.1016/j.artmed.2022.102347 https://doi.org/10.1016/j.artmed.2022.102347
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spelling	my.ump.umpir.401822024-02-08T00:53:45Z http://umpir.ump.edu.my/id/eprint/40182/ Cardiovascular disease detection from high utility rare rule mining Iqbal, Mohammad Setiawan, Muhammad Nanda Isa Irawan, Mohammad Isa Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Q Science (General) QA Mathematics We propose a method to search rare cardiovascular disease symptom rules from historical health examination records according to its hazard ratio utility and further detect the disease given new medical record data. Further, we aim to assist both medical experts and patients by alerting the current symptoms and preparing the early treatments. In general, the proposed method first deals with the uncertainty of age and other continuous features using a fuzzy set. Next, we define the hazard ratio utility of each item set to assist the mining process. Based on the utility, we discover the rare cardiovascular disease patterns employing High Utility Rare Itemset Mining. At last, we add a prediction step to check the given health record data whether diagnosed cardiovascular. Subsequently, we can obtain rare symptoms of cardiovascular disease, which are later applied to detect the new related record data. The rare symptoms that are confirmed by their utility risk for cardiovascular disease can assist the medical experts' decision better than the common symptoms as it is often hard to be recognized at a glance. The proposed method evaluated on a public cardiovascular dataset. The experimental results showed that the generated rare cardiovascular disease patterns successfully applied to detect the cardiovascular given the symptoms data. Elsevier B.V. 2022-09 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40182/1/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare.pdf pdf en http://umpir.ump.edu.my/id/eprint/40182/2/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare%20rule%20mining_ABS.pdf Iqbal, Mohammad and Setiawan, Muhammad Nanda and Isa Irawan, Mohammad Isa and Ku Muhammad Naim, Ku Khalif and Noryanti, Muhammad and Mohd Khairul Bazli, Mohd Aziz (2022) Cardiovascular disease detection from high utility rare rule mining. Artificial Intelligence in Medicine, 131 (102347). pp. 1-12. ISSN 0933-3657. (Published) https://doi.org/10.1016/j.artmed.2022.102347 https://doi.org/10.1016/j.artmed.2022.102347
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 English
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Iqbal, Mohammad Setiawan, Muhammad Nanda Isa Irawan, Mohammad Isa Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz Cardiovascular disease detection from high utility rare rule mining
description	We propose a method to search rare cardiovascular disease symptom rules from historical health examination records according to its hazard ratio utility and further detect the disease given new medical record data. Further, we aim to assist both medical experts and patients by alerting the current symptoms and preparing the early treatments. In general, the proposed method first deals with the uncertainty of age and other continuous features using a fuzzy set. Next, we define the hazard ratio utility of each item set to assist the mining process. Based on the utility, we discover the rare cardiovascular disease patterns employing High Utility Rare Itemset Mining. At last, we add a prediction step to check the given health record data whether diagnosed cardiovascular. Subsequently, we can obtain rare symptoms of cardiovascular disease, which are later applied to detect the new related record data. The rare symptoms that are confirmed by their utility risk for cardiovascular disease can assist the medical experts' decision better than the common symptoms as it is often hard to be recognized at a glance. The proposed method evaluated on a public cardiovascular dataset. The experimental results showed that the generated rare cardiovascular disease patterns successfully applied to detect the cardiovascular given the symptoms data.
format	Article
author	Iqbal, Mohammad Setiawan, Muhammad Nanda Isa Irawan, Mohammad Isa Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz
author_facet	Iqbal, Mohammad Setiawan, Muhammad Nanda Isa Irawan, Mohammad Isa Ku Muhammad Naim, Ku Khalif Noryanti, Muhammad Mohd Khairul Bazli, Mohd Aziz
author_sort	Iqbal, Mohammad
title	Cardiovascular disease detection from high utility rare rule mining
title_short	Cardiovascular disease detection from high utility rare rule mining
title_full	Cardiovascular disease detection from high utility rare rule mining
title_fullStr	Cardiovascular disease detection from high utility rare rule mining
title_full_unstemmed	Cardiovascular disease detection from high utility rare rule mining
title_sort	cardiovascular disease detection from high utility rare rule mining
publisher	Elsevier B.V.
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/40182/1/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare.pdf http://umpir.ump.edu.my/id/eprint/40182/2/Cardiovascular%20disease%20detection%20from%20high%20utility%20rare%20rule%20mining_ABS.pdf http://umpir.ump.edu.my/id/eprint/40182/ https://doi.org/10.1016/j.artmed.2022.102347 https://doi.org/10.1016/j.artmed.2022.102347
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score	13.235362

Cardiovascular disease detection from high utility rare rule mining

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