Published Paper Details:

In the modern period, people are afflicted with numerous ailments. Finding treatments for these illnesses or ways to identify them in their earliest stages has become essential for their prevention or treatment. One of the main causes of blindness and one of the eye illnesses is glaucoma. It is a disease that causes the patient's ocular vessels to slowly deteriorate, impairing eyesight. The data mining methods used to diagnose glaucoma in retinal images, including Decision Tree, Linear Regression, and Support Vector Machine, are discussed in this work. To evaluate each technique's effectiveness in terms of accuracy, sensitivity, and specificity, parameters from perimetry and the Stratus Optic Coherence Test (OCT) were supplied into it. The accuracy of the decision tree and linear regression models for the diagnosis of glaucoma is 99.17%, 92.56%, and 70.25 percent, respectively. The researcher compared the outcomes obtained from the decision tree, linear regression, and support vector machine (SVM), and discovered that they perform much better than SVM. SVM and linear regression have specificities of 97.56% and 96.34%, respectively.