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Author(s):

VIJAY.S
Keywords:

Artificial Intelligence, Clinical Data Analytics, Ocular Disease Diagnosis, Machine Learning, Random Forest, SVM, XGBoost, Django.
Abstract:
Vision-threatening Ocular Diseases Such As Diabetic Retinopathy, Glaucoma, Cataract, Age-related Macular Degeneration (AMD), Hypertensive Retinopathy And Pathological Myopia Are Leading Causes Of Preventable Blindness Worldwide. Conventional Diagnosis Depends On Manual Examination By Ophthalmologists, Which Is Time-consuming, Costly And Constrained By The Limited Availability Of Specialists, Particularly In Rural Areas. This Paper Presents An AI-powered Diagnostic Framework That Applies Clinical Data Analytics And Machine Learning To Classify Ocular Conditions Using Patient Parameters Such As Intraocular Pressure, Visual Acuity, Blood Pressure, Diabetes Status, Family History And Symptom Duration. Algorithms Including Random Forest, Support Vector Machine (SVM), Decision Tree, Bagging Classifier And XGBoost Were Implemented And Integrated Into A Django-based Web Application That Provides Registration, Login, Data Entry, Prediction And Report Generation Modules. Experimental Evaluation Achieved An Accuracy Of 96%, Precision Of 95%, Recall Of 94% And F1-score Of 95%, Demonstrating That The Proposed System Can Assist Ophthalmologists In Early Detection, Reduce Diagnostic Time And Improve Healthcare Accessibility.