Improving Diabetic Diagnosis and Prevention with Machine Learning on Retinal Imaging

American Heritage School Boca/Delray, Florida, United States

^* Corresponding author: jessiemin2005@gmail.com

Abstract

If the retinal images show evidences of abnormalities such as change in volume, diameter, and unusual spots in the retina, then there is a positive correlation to the diabetic progress. Mathematical and statistical theories behind the machine learning algorithms are powerful enough to detect signs of diabetes through retinal images. Several machine learning algorithms: Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks were applied to predict whether images contain signs of diabetic retinopathy or not. After building the models, the computed results of these algorithms were compared by confusion matrixes, receiver operating characteristic curves, and Precision-Recall curves. The performance of the Support Vector Machine algorithm was the best since it had the highest true-positive rate, area under the curve for ROC curve, and area under the curve for Precision-Recall curve. This conclusion shows that the most complex algorithms doesn’t always give the best performance, the final accuracy also depends on the dataset. For this dataset of retinal imaging, the Support Vector Machine algorithm achieved the best results. Detecting signs of diabetic retinopathy is helpful for detecting for diabetes since more than 60% of patients with diabetes have signs of diabetic retinopathy. Machine learning algorithms can speed up the process and improve the accuracy of diagnosis. When the method is reliable enough, it can be utilized in diabetes diagnosis directly in clinics. Current methods require going on diets and taking blood samples, which could be very time consuming and inconvenient. Using machine learning algorithms is fast and noninvasive compared to the existing methods. The purpose of this research was to build an optimized model by machine learning algorithms that can improve the diagnosis accuracy and classification of patients at high risk of diabetes using retinal imaging.