{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T18:26:00Z","timestamp":1761157560050,"version":"3.40.5"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Cataracts are the problems associated with the crystallins proteins of the eye lens. Any perturbation in the conformity of these proteins results in a cataract. Age-related cataract is the most common type among all cataracts as it accounts for almost 80% of cases of senile blindness worldwide. This research study was performed to predict the role of single nucleotide polymorphisms (SNPs) of the <jats:italic>GJA8<\/jats:italic> gene with age-related cataracts in 718 subjects (400 age-related cataract patients and 318 healthy individuals). A comparison of supervised machine learning classification algorithm including logistic regression (LR), random forest (RF) and Artificial Neural Network (ANN) were presented to predict the age-related cataracts. The results indicated that LR is the best for predicting age-related cataracts. This successfully developed model after accounting different genetic and demographic factors to predict cataracts will help in effective disease management and decision-making medical practitioner and experts.<\/jats:p>","DOI":"10.1371\/journal.pone.0286243","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T17:38:21Z","timestamp":1693503501000},"page":"e0286243","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Potential of GJA8 gene variants in predicting age-related cataract: A comparison of supervised machine learning methods"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-0084","authenticated-orcid":true,"given":"Saba","family":"Zafar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-4742","authenticated-orcid":true,"given":"Haris","family":"Khurram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Kamran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madeeha","family":"Fatima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aqsa","family":"Parvaiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3648-6965","authenticated-orcid":true,"given":"Rehan Sadiq","family":"Shaikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"issue":"11","key":"pone.0286243.ref001","doi-asserted-by":"crossref","first-page":"e112054","DOI":"10.1371\/journal.pone.0112054","article-title":"Age-related cataract, cataract surgery and subsequent mortality: a systematic review and meta-analysis","volume":"9","author":"E Song","year":"2014","journal-title":"PLoS One"},{"issue":"1","key":"pone.0286243.ref002","first-page":"1","article-title":"Cataract.","volume":"1","author":"D Lam","year":"2015","journal-title":"Nature reviews Disease primers"},{"issue":"1","key":"pone.0286243.ref003","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ajo.2012.01.033","article-title":"The impact of cataract, cataract types, and cataract grades on vision-specific functioning using Rasch analysis","volume":"154","author":"M Chew","year":"2012","journal-title":"American journal of Ophthalmology"},{"issue":"4","key":"pone.0286243.ref004","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jcjo.2013.03.010","article-title":"Controversies in traumatic cataract classification and management: a review","volume":"48","author":"M Shah","year":"2013","journal-title":"Canadian Journal of Ophthalmology"},{"issue":"8","key":"pone.0286243.ref005","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1016\/j.ophtha.2016.04.037","article-title":"Risk factors for cataracts treated surgically in postmenopausal women","volume":"123","author":"S Floud","year":"2016","journal-title":"Ophthalmology"},{"issue":"12","key":"pone.0286243.ref006","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1016\/j.ophtha.2018.07.026","article-title":"Six-year incidence of and risk factors for cataract surgery in a multi-ethnic Asian population: the Singapore Epidemiology of Eye Diseases Study","volume":"125","author":"AG Tan","year":"2018","journal-title":"Ophthalmology"},{"key":"pone.0286243.ref007","article-title":"Predicting the 10-year risk of cataract surgery using machine learning techniques on questionnaire data: findings from the 45 and Up Study","author":"W Wang","year":"2021","journal-title":"British Journal of Ophthalmology"},{"issue":"4","key":"pone.0286243.ref008","doi-asserted-by":"crossref","first-page":"477","DOI":"10.4103\/ijo.IJO_1127_17","article-title":"The prevalence and risk factors for cataract in rural and urban India","volume":"67","author":"S Singh","year":"2019","journal-title":"Indian journal of ophthalmology"},{"issue":"12","key":"pone.0286243.ref009","first-page":"6253","article-title":"Prevalence and risk factors for cataract in diabetes: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study, report no. 17. 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