The prediction of glaucoma from ocular biometric data. Part 1. An application of multiple regression analysis.
Two sets of multiple regression equations (prediction systems) were derived from the analysis of ocular biometric data obtained from glaucoma patients (16 open angle; 16 angle-closure) and 75 normal subjects. Discriminant scores were established for both sets of equations which minimised the number of false negatives. One set, the 'Glaucoma Equations' was applied to the data to segregate the glaucoma from the normal subjects. The other prediction system, the 'Classification Equations,' was then applied to the group defined as glaucomatous to discriminate between patients in the angle-closure and open angle categories. The performance of these equations, obtained by comparing the predicted and actual classifications for this sample, was such that between 9 and 12% of false positives and 0 and 3% false negatives were found on the 'Glaucoma Equations' and between 6 and 12% of false positives with no false negatives on the 'Classification Equations.'
D000367:Age Factors; D000867:Anterior Chamber / Q000033:anatomy & histology; D000886:Anthropometry / Q000379:methods; D003315:Cornea / Q000033:anatomy & histology; D005123:Eye / Q000033:anatomy & histology*; D005260:Female; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure; D007908:Lens, Crystalline / Q000033:anatomy & histology; D008297:Male; D011379:Prognosis; D012029:Refraction, Ocular; D012044:Regression Analysis; D012737:Sex Factors; D013223:Statistics as Topic; D014822:Vitreous Body / Q000033:anatomy & histology