Performance Analysis of Diabetic Retinopathy Prediction using Machine Learning Models

Abstract

Diabetic Retinopathy (DR) is a symptom of diabetes that affects the eyes. The blood vessels of the light tissue behind the eyes are damaged (retina). Machine Learning (ML) techniques play a vital role in computer aid diagnosis and discover successful systems for detecting life-threatening diseases. This research aimed to predict diabetic retinopathy and also implement feature extraction to figure out some features. In this research, the data is collected from the UCI machine learning repository. Several Machine Learning (ML) techniques are used for analysis this dataset and find out the best performance and sensitivity, selectivity, true positive (tp) rate, false negative (fn) rate and receiver operating characteristic (roc) curve. In this study, some machine learning algorithms are used such as Naive Bayes, Sequential Minimal Optimization (SMO), logistic regression, Stochastic Gradient Descent (SGD), bagging classifier, J48 classifier, decision tree classifier, and random forest classifier. The overall performance of logistic regression shows the best result.