Exploiting the retinal vascular geometry in identifying the progression to diabetic retinopathy using penalized logistic regression and random forests

Leontidis, Georgios and Al-Diri, Bashir and Hunter, Andrew (2016) Exploiting the retinal vascular geometry in identifying the progression to diabetic retinopathy using penalized logistic regression and random forests. In: Emerging trends and advanced technologies for computational intelligence. Studies in Computational Intelligence, 647 . Springer International Publishing, Switzerland, pp. 381-400. ISBN 9783319333533, 9783319333519

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Abstract

Many studies have been conducted, investigating the effects that diabetes has to the retinal vasculature. Identifying and quantifying the retinal vascular changes remains a very challenging task, due to the heterogeneity of the retina. Monitoring the progression requires follow-up studies of progressed patients, since human retina naturally adapts to many different stimuli, making it hard to associate any changes with a disease. In this novel study, data from twenty five diabetic patients, who progressed to diabetic retinopathy, were used. The progression was evaluated using multiple geometric features, like vessels widths and angles, tortuosity, central retinal artery and vein equivalent, fractal dimension, lacunarity, in addition to the corresponding descriptive statistics of them. A statistical mixed model design was used to evaluate the significance of the changes between two periods: 3 years before the onset of diabetic
retinopathy and the first year of diabetic retinopathy. Moreover, the discriminative power of these features was evaluated using a random forests classifier and also a penalized logistic regression. The area under the ROC curve after running a ten-fold cross validation was 0.7925 and 0.785 respectively.

Keywords:diabetic retinopathy, Machine Learning, statistical analysis, bmjdoi
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:22832
Deposited On:26 Apr 2016 20:32

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