Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

Introduction. 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 inv...

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Autores principales:	Fatin Nabihah Jais, Mohd Zulfaezal Che Azemin, Mohd Radzi Hilmi, Mohd Izzuddin Mohd Tamrin, Khairidzan Mohd Kamal
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology T Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d00da79909a3453bbe4a9f43ff947a5c
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spelling	oai:doaj.org-article:d00da79909a3453bbe4a9f43ff947a5c2021-11-29T00:55:41ZPostsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique1537-744X10.1155/2021/6211006https://doaj.org/article/d00da79909a3453bbe4a9f43ff947a5c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6211006https://doaj.org/toc/1537-744XIntroduction. 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.Fatin Nabihah JaisMohd Zulfaezal Che AzeminMohd Radzi HilmiMohd Izzuddin Mohd TamrinKhairidzan Mohd KamalHindawi LimitedarticleTechnologyTMedicineRScienceQENThe Scientific World Journal, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Medicine R Science Q
spellingShingle	Technology T Medicine R Science Q Fatin Nabihah Jais Mohd Zulfaezal Che Azemin Mohd Radzi Hilmi Mohd Izzuddin Mohd Tamrin Khairidzan Mohd Kamal Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique
description	Introduction. 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	Fatin Nabihah Jais Mohd Zulfaezal Che Azemin Mohd Radzi Hilmi Mohd Izzuddin Mohd Tamrin Khairidzan Mohd Kamal
author_facet	Fatin Nabihah Jais Mohd Zulfaezal Che Azemin Mohd Radzi Hilmi Mohd Izzuddin Mohd Tamrin Khairidzan Mohd Kamal
author_sort	Fatin Nabihah Jais
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 Limited
publishDate	2021
url	https://doaj.org/article/d00da79909a3453bbe4a9f43ff947a5c
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_version_	1718407798083026944

Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

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