An Improved Prediction Approach for Progression of Ocular Hypertension to Primary Open Angle Glaucoma
In this paper, we present an improved prediction model for progression of ocular hypertension to primary open angle glaucoma using a random forest classification approach. Our model comprises two phases: risk factor calculation and prediction. We start by calculating the risk factors associated with the outcome, followed by a prediction phase that utilises a random forest approach for classification into one of four obtained classes, namely low, mid, high, and moderate. Experimental results show that the employed random forest classifier provides better prediction accuracy compared to other machine learning techniques including Bayes net, multi-layer perceptron, radial basis function and naive Bayes tree classifiers.
KeywordsGlaucoma primary open angle glaucoma retinal fiber layer machine learning pattern classification random forest classification
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