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Automated Early Prediction of Anomalies Due to Diabetes Using Fundus Images

Diabetes is one of the common diseases in the world that cannot be permanently cured, but with proper medication one can lead a long and healthy life by curbing extreme complications. The skills and equipment required to identify the conditions take a longer time to provide an accurate result and are not an affordable means for all the income groups. In order to overcome this issue, an ML model is created and deployed in an application so it will be used by many in predicting the presence of the disease. The chapter focuses on detecting the presence of two major anomalies, namely diabetic retinopathy (DR) and glaucoma, which were caused due to diabetes. All the dataset used for the project is gathered from Kaggle and Messidor. Around six machine learning algorithms that fall under supervised learning techniques are executed. Among the many models, the random forest model has a high accuracy of 73% for DR prediction. Simultaneously, glaucoma detection is performed using different algorithms showing that Naive Bayes has the highest accuracy of 98%.