Eighteenth International Conference on Technology, Knowledge, and Society

Abstract

Knowledge Discovery in Database (KDD) and is a process which includes extracting the interesting, interpretable and useful information from the raw data. Glaucoma affects through increasing an intraocular pressure (IOP), which is responsible for the glaucomatous optic neuropathy involving the death of retinal ganglion cells and their axons which resulting in blindness. A person, who is responsible to treat glaucoma patients might make an error in glaucoma type identifying and treatment ordering due to subjective decision, knowledge limitation, and visualization through instruments. This results in resource wastage as well as time-consuming. The main aim of this research is to reduce (not remove the problem of) the biased decision of the ophthalmologist through making an easy, quick and accessible way for glaucoma type identification by developing a better classification model. In this study, data mining techniques are used to discover new knowledge based on the collected dataset. From numerous data mining classification algorithms, this paper applies naïve Baye, grip, J48, and PART algorithms that are used with two basic test options based on complete and selected features. Based on the experiential analysis performed, the PART algorithm with a test option of 10 fold cross-validation using the selected feature scored the highest accuracy result which is 71.4%.