Combining Multiple HRT Parameters Using the ‘Random Forests' Method Improves the Diagnostic Accuracy of Glaucoma in Emmetropic and Highly Myopic Eyes

Abstract

To combine multiple Heidelberg Retina Tomograph (HRT) parameters using the Random Forests classifier to diagnose glaucoma, both in highly and physiologically myopic (highly myopic) eyes and emmetropic eyes. Subjects consisted of healthy subjects and age-matched patients with open-angle glaucoma in emmetropic (-1.0 to +1.0 diopters [D], 63 and 59 subjects, respectively) and highly myopic eyes (-10.0 to -5.0 D, 56 and 64 subjects, respectively). First, area under the receiver operating characteristic curve (AUC) was derived using 84 HRT global and sectorial parameters and the representative HRT raw parameter (largest AUC) was identified. Then, the Random Forests method was carried out using age, refractive error, and 84 HRT parameters. The AUCs were also derived using the following: (1) Frederick S. Mikelberg discriminant function (FSM) score, (2) Reinhard O.W. Burk discriminant function (RB) score, (3) Moorfields regression analysis (MRA) score, and (4) glaucoma probability score (GPS). In combined emmetropic and highly myopic population, AUC with Random Forests method (0.96) was significantly larger than AUCs with the representative HRT raw parameter (vertical cup-to-disc ratio [global], 0.89), FSM (0.90), RB (0.83), MRA (0.87), and GPS (0.81) (P < 0.001). Similarly, AUC with the Random Forests method was significantly (P < 0.05) larger than these other parameters, both in emmetropic and highly myopic groups. Also, the Random Forests method achieved partial AUCs above 80%/90% significantly (P < 0.05) larger than any other HRT parameters in all populations. Evaluating multiple HRT parameters using the Random Forests classifier provided accurate diagnosis of glaucoma, both in emmetropic and highly myopic eyes.