{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:32:42Z","timestamp":1777987962386,"version":"3.51.4"},"reference-count":18,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KICT Research Initiatives","award":["KICT-RG20-005-0005"],"award-info":[{"award-number":["KICT-RG20-005-0005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Scientific World Journal"],"published-print":{"date-parts":[[2021,11,15]]},"abstract":"<jats:p>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%\u2009\u00b1\u20095.86%), specificity (100%), and sensitivity (92.14%\u2009\u00b1\u20098.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.<\/jats:p>","DOI":"10.1155\/2021\/6211006","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T23:05:36Z","timestamp":1637017536000},"page":"1-7","source":"Crossref","is-referenced-by-count":10,"title":["Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8482-2682","authenticated-orcid":true,"given":"Fatin Nabihah","family":"Jais","sequence":"first","affiliation":[{"name":"Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-0822","authenticated-orcid":true,"given":"Mohd Zulfaezal","family":"Che Azemin","sequence":"additional","affiliation":[{"name":"Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6352-5152","authenticated-orcid":true,"given":"Mohd Radzi","family":"Hilmi","sequence":"additional","affiliation":[{"name":"Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-8174","authenticated-orcid":true,"given":"Mohd Izzuddin","family":"Mohd Tamrin","sequence":"additional","affiliation":[{"name":"Kulliyyah of ICT, International Islamic University Malaysia, Gombak, Kuala Lumpur 50728, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9106-8913","authenticated-orcid":true,"given":"Khairidzan Mohd","family":"Kamal","sequence":"additional","affiliation":[{"name":"Kulliyyah of Medicine, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"1","key":"1","first-page":"341","article-title":"Pterygium and induced astigmatism","volume":"15","author":"Q. 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