Research Article Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique Fatin Nabihah Jais , 1 Mohd Zulfaezal Che Azemin , 1 Mohd Radzi Hilmi , 1 Mohd Izzuddin Mohd Tamrin , 2 and Khairidzan Mohd Kamal 3 1 Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia 2 Kulliyyah of ICT, International Islamic University Malaysia, Gombak, Kuala Lumpur 50728, Malaysia 3 Kulliyyah of Medicine, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia Correspondence should be addressed to Mohd Zulfaezal Che Azemin; zulfaezal@iium.edu.my Received 23 August 2021; Accepted 29 October 2021; Published 15 November 2021 Academic Editor: Ahmad Mansour Copyright © 2021 Fatin Nabihah Jais et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. e 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. e 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. 1. Introduction Pterygium is an ocular pathology where a form of tri- angular or wing-shaped fibrovascular connective tissue grows from the limbus and covers the corneal surface [1]. e interference of the tissue towards the cornea leads to the visual disruption that causes irritation, inflammation, tearing, dryness, and itchiness. e etiology of pterygium is still unknown; however, excessive ultraviolet (UV) light exposure due to hot climates or spending lots of time in a humid sunny environment is the prominent factor that contributes to this condition [2]. Pterygium can be cat- egorized as inactive or active depending on its progression over time, developing corneal distortion when the active form occurs [1]. e main concern is a significant re- fractive change that eventually results in visual impair- ment if it remains untreated for a long time. Refractive change due to pterygium is characterized as corneal astigmatism due to flattening of the cornea, and it is treatable since the affected vision from the active growth of the conjunctiva tissue can be managed accordingly with surgical intervention [1]. Hindawi e Scientific World Journal Volume 2021, Article ID 6211006, 7 pages https://doi.org/10.1155/2021/6211006