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

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

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Abstract

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.

Item Type:	Article (Journal)
Subjects:	R Medicine > RE Ophthalmology T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Allied Health Sciences Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Medicine Kulliyyah of Medicine > Department of Ophthalmology
Depositing User:	Mohd Izzuddin Mohd Tamrin
Date Deposited:	16 Nov 2021 16:55
Last Modified:	30 Dec 2021 16:35
URI:	http://irep.iium.edu.my/id/eprint/93778

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