Potential of GJA8 gene variants in predicting age-related cataract: A comparison of supervised machine learning methods

• Published in: PloS one Vol. 18; no. 8; p. e0286243
• Main Authors: Zafar, Saba, Khurram, Haris, Kamran, Muhammad, Fatima, Madeeha, Parvaiz, Aqsa, Shaikh, Rehan Sadiq
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 31.08.2023; Public Library of Science (PLoS)
• Subjects: Age; Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Blindness; Cataract; Cataracts; Comparative analysis; Computer and Information Sciences; Congenital diseases; Decision making; Decision trees; Development and progression; Diagnosis; Ethylenediaminetetraacetic acid; Eye lens; Genes; Genetic aspects; Health risk assessment; Health risks; Learning algorithms; Machine learning; Medical research; Medicine and Health Sciences; Medicine, Experimental; Methods; Mutation; Neural networks; Nucleotides; Ophthalmology; Perturbation; Proteins; Research and Analysis Methods; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Supervised learning; Surgery; Pakistan; China; India; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0286243

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 GJA8 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.