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Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer

Lung cancer constituted 12.2% of newly diagnosed cancer cases globally in 2020. The high fatality rate of the condition is attributed to delayed diagnosis and inadequate symptom recognition. In Malaysia, the incidence of lung cancer is estimated to be 1 in 60 males and 1 in 138 females, with a medi...

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Main Authors: Zakaria, Aliya Syaffa, Shaf, Muhammad Ammar, Mohd Zim, Mohd Arif, Musa, Aisya Natasya
Format: Article
Language:English
Published: IOS Press 2024
Subjects:
RC Internal medicine
Online Access:http://eprints.uthm.edu.my/12127/1/J17689_fec3f32e3ccce0a35bd05d0f1d3e97b3.pdf
http://eprints.uthm.edu.my/12127/
https://doi.org/10.3233/JIFS-233714
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spelling my.uthm.eprints.121272024-12-01T04:07:40Z http://eprints.uthm.edu.my/12127/ Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer Zakaria, Aliya Syaffa Shaf, Muhammad Ammar Mohd Zim, Mohd Arif Musa, Aisya Natasya RC Internal medicine Lung cancer constituted 12.2% of newly diagnosed cancer cases globally in 2020. The high fatality rate of the condition is attributed to delayed diagnosis and inadequate symptom recognition. In Malaysia, the incidence of lung cancer is estimated to be 1 in 60 males and 1 in 138 females, with a median age of 70 years or above. Most lung cancer cases were detected during advanced stages, specifically stages III and IV, with a prevalence exceeding 90% for both genders. In Malaysia, most patients are diagnosed in stages III and IV, which are associated with a lower likelihood of long-term survival. Many cases are identified at a late stage, characterized by significant tumor expansion or the spread of cancer cells to areas that cannot be treated surgically. Malaysians are unaware of cancer symptoms; hence the situation is common. To improve survival and reduce mortality, Malaysians must recognize the symptoms of lung cancer. Fuzzy linear regression and multiple linear regression models have been compared to predict high-risk lung cancer symptoms in Malaysia. The fuzzy linear regression model analyses secondary data, eliminates irrelevant information and enhances precision in the results. Lung cancer patients at Al-Sultan Abdullah Hospital (UiTM Hospital) in Selangor provided data for this study. Data from 124 lung cancer patients were analyzed using Microsoft Excel, SPSS, and MATLAB. To improve data accuracy, the study used cross-validation measurement error (MSE and RMSE). According to data analysis, hemoptysis and chest pain are high-risk symptoms with MSE and RMSE values of 1.549 and 1.245, respectively IOS Press 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12127/1/J17689_fec3f32e3ccce0a35bd05d0f1d3e97b3.pdf Zakaria, Aliya Syaffa and Shaf, Muhammad Ammar and Mohd Zim, Mohd Arif and Musa, Aisya Natasya (2024) Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer. Journal of Intelligent & Fuzzy Systems, 46. pp. 7959-7968. https://doi.org/10.3233/JIFS-233714
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic RC Internal medicine
spellingShingle RC Internal medicine
Zakaria, Aliya Syaffa
Shaf, Muhammad Ammar
Mohd Zim, Mohd Arif
Musa, Aisya Natasya
Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
description Lung cancer constituted 12.2% of newly diagnosed cancer cases globally in 2020. The high fatality rate of the condition is attributed to delayed diagnosis and inadequate symptom recognition. In Malaysia, the incidence of lung cancer is estimated to be 1 in 60 males and 1 in 138 females, with a median age of 70 years or above. Most lung cancer cases were detected during advanced stages, specifically stages III and IV, with a prevalence exceeding 90% for both genders. In Malaysia, most patients are diagnosed in stages III and IV, which are associated with a lower likelihood of long-term survival. Many cases are identified at a late stage, characterized by significant tumor expansion or the spread of cancer cells to areas that cannot be treated surgically. Malaysians are unaware of cancer symptoms; hence the situation is common. To improve survival and reduce mortality, Malaysians must recognize the symptoms of lung cancer. Fuzzy linear regression and multiple linear regression models have been compared to predict high-risk lung cancer symptoms in Malaysia. The fuzzy linear regression model analyses secondary data, eliminates irrelevant information and enhances precision in the results. Lung cancer patients at Al-Sultan Abdullah Hospital (UiTM Hospital) in Selangor provided data for this study. Data from 124 lung cancer patients were analyzed using Microsoft Excel, SPSS, and MATLAB. To improve data accuracy, the study used cross-validation measurement error (MSE and RMSE). According to data analysis, hemoptysis and chest pain are high-risk symptoms with MSE and RMSE values of 1.549 and 1.245, respectively
format Article
author Zakaria, Aliya Syaffa
Shaf, Muhammad Ammar
Mohd Zim, Mohd Arif
Musa, Aisya Natasya
author_facet Zakaria, Aliya Syaffa
Shaf, Muhammad Ammar
Mohd Zim, Mohd Arif
Musa, Aisya Natasya
author_sort Zakaria, Aliya Syaffa
title Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
title_short Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
title_full Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
title_fullStr Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
title_full_unstemmed Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
title_sort comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer
publisher IOS Press
publishDate 2024
url http://eprints.uthm.edu.my/12127/1/J17689_fec3f32e3ccce0a35bd05d0f1d3e97b3.pdf
http://eprints.uthm.edu.my/12127/
https://doi.org/10.3233/JIFS-233714
_version_ 1817845168855842816
score 13.235796

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