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Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique

Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibro...

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Main Authors: Jais, Fatin Nabihah, Che Azemin, Mohd Zulfaezal, Hilmi, Mohd Radzi, Mohd Tamrin, Mohd Izzuddin, Mohd. Kamal, Khairidzan
Format: Article
Language:English
English
Published: Hindawi 2021
Subjects:
RE Ophthalmology
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/93778/7/93778_Postsurgery%20Classification%20of%20Best-Corrected%20Visual%20Acuity%20Changes%20Based%20on%20Pterygium%20Characteristics.pdf
http://irep.iium.edu.my/93778/13/93778_Postsurgery%20classification%20of%20best-corrected_Scopus.pdf
http://irep.iium.edu.my/93778/
https://www.hindawi.com/journals/tswj/2021/6211006/
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spelling my.iium.irep.937782021-12-30T08:35:03Z http://irep.iium.edu.my/93778/ Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique Jais, Fatin Nabihah Che Azemin, Mohd Zulfaezal Hilmi, Mohd Radzi Mohd Tamrin, Mohd Izzuddin Mohd. Kamal, Khairidzan RE Ophthalmology TK7885 Computer engineering Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics. Hindawi 2021-11-15 Article PeerReviewed application/pdf en http://irep.iium.edu.my/93778/7/93778_Postsurgery%20Classification%20of%20Best-Corrected%20Visual%20Acuity%20Changes%20Based%20on%20Pterygium%20Characteristics.pdf application/pdf en http://irep.iium.edu.my/93778/13/93778_Postsurgery%20classification%20of%20best-corrected_Scopus.pdf Jais, Fatin Nabihah and Che Azemin, Mohd Zulfaezal and Hilmi, Mohd Radzi and Mohd Tamrin, Mohd Izzuddin and Mohd. Kamal, Khairidzan (2021) Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique. The Scientific World Journal, 2021. pp. 1-7. ISSN 2356-6140 E-ISSN 1537-744X https://www.hindawi.com/journals/tswj/2021/6211006/ 10.1155/2021/6211006
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RE Ophthalmology
TK7885 Computer engineering
spellingShingle RE Ophthalmology
TK7885 Computer engineering
Jais, Fatin Nabihah
Che Azemin, Mohd Zulfaezal
Hilmi, Mohd Radzi
Mohd Tamrin, Mohd Izzuddin
Mohd. Kamal, Khairidzan
Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
description Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
format Article
author Jais, Fatin Nabihah
Che Azemin, Mohd Zulfaezal
Hilmi, Mohd Radzi
Mohd Tamrin, Mohd Izzuddin
Mohd. Kamal, Khairidzan
author_facet Jais, Fatin Nabihah
Che Azemin, Mohd Zulfaezal
Hilmi, Mohd Radzi
Mohd Tamrin, Mohd Izzuddin
Mohd. Kamal, Khairidzan
author_sort Jais, Fatin Nabihah
title Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
title_short Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
title_full Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
title_fullStr Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
title_full_unstemmed Postsurgery classification of best-corrected visual acuity changes based on Pterygium Characteristics using the Machine Learning Technique
title_sort postsurgery classification of best-corrected visual acuity changes based on pterygium characteristics using the machine learning technique
publisher Hindawi
publishDate 2021
url http://irep.iium.edu.my/93778/7/93778_Postsurgery%20Classification%20of%20Best-Corrected%20Visual%20Acuity%20Changes%20Based%20on%20Pterygium%20Characteristics.pdf
http://irep.iium.edu.my/93778/13/93778_Postsurgery%20classification%20of%20best-corrected_Scopus.pdf
http://irep.iium.edu.my/93778/
https://www.hindawi.com/journals/tswj/2021/6211006/
_version_ 1720979908191059968
score 13.211869

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