Early Detection Of Diabetic Retinopathy Research Articles

Comparative study on early recognition and identifying diabetic retinopathy with different layers in CNN

Diabetes is the most prevalent condition worldwide, and diabetic retinopathy (DR) is a subsequent condition caused by acute diabetic cases. It causes severe degeneration of the retina. The compounding blood vessels bloat and often burst, causing fluid leaks in the aqueous humor. This, in turn, causes the creation of undesirable nerve fiber infractions from the occlusion of arteries. Diagnosis requires a manual retinal examination that can often be inconsistent and deliberate with potential flaws in the diagnosis. Early detection through an ophthalmologist is paramount to prevent the prognosis of severe vision loss. Considering the current leap of machine learning in the field of healthcare, early detection of DR can be potentially made efficient with intelligent systems. This research proposes methodologies to fine-tune the existing pre-trained architectures, attaining the classification accuracies of 98% to classify the ocular fundus images which identify early prediction of diabetes. Additionally, this study presents an exposition of other equally scrutinized approaches to ultimately showcase a deep neural network architecture that can precisely classify normal fundus and degenerated fundus from the lowest to the most severe hierarchy. Among several layers in the CNN model pre-tuning and post-tuning exception layers outperformed with good results.

A deep transfer learning approach for identification of diabetic retinopathy using data augmentation

In ophthalmology, deep learning acts as a computer-based tool with numerous potential capabilities and efficacy. Throughout the world, diabetic retinopathy (DR) is considered as a principal cause of disease however loss of sight cannot be seen in adults aged 20-74 years. The primary objective for early detection of DR is screening on a regular basis at separate intervals which should have a time difference of every ten to twenty months for the patients with no or mild case of DR. Regular screening plays a major role to prevent vision loss, the expected cases increase from 415 million in 2015 to 642 million in 2040 means is a challenging task of ophthalmologists to do screening and follow-up representations. In this research, a transfer learning model was proposed with data augmentation techniques and gaussian-blur, circle-crop pre-processing techniques combination to identify every stage of DR using Resnet 50 with top layers. Models are prepared with Kaggle Asia Pacific Tele-Ophthalmology Society blindness dataset on a top line graphical processing data. The result depicts- the comparison of classification metrics using synthetic and non-synthetic images and achieve accuracy of 91% using the synthetic data and 86% accuracy without using synthetic data.

Early Detection of Diabetic Retinopathy Based Artificial Intelligent Techniques

The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks (BPNN) and Support Vector Machine (SVM) classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall. The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks and Support Vector Machine classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall..

Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study.

To identify an optimal model for diabetic retinopathy (DR) prediction in Chinese rural population by establishing and comparing different algorithms based on the data from Handan Eye Study (HES). Five algorithms, including multivariable logistic regression (MLR), classification and regression trees (C&RT), support vector machine (SVM), random forests (RF), and gradient boosting machine (GBM), were used to establish DR prediction models with HES data. The performance of the models was assessed based on the adjusted area under the ROC curve (AUROC), sensitivity, specificity, and accuracy. The data on 4752 subjects were used to build the DR prediction model, and among them, 198 patients were diagnosed with DR. The age of the included subjects ranged from 30 to 85 years old, with an average age of 50.9 years (SD = 3.04). The kappa coefficient of the diagnosis between the two ophthalmologists was 0.857. The MLR model revealed that blood glucose, systolic blood pressure, and body mass index were independently associated with the development of DR. The AUROC obtained by GBM (0.952), RF (0.949), and MLR (0.936) was similar and statistically larger than that of CART (0.682) and SVM (0.765). The MLR model exhibited excellent prediction performance and visible equation and thus was the optimal model for DR prediction. Therefore, the MLR model may have the potential to serve as a complementary screening tool for the early detection of DR, especially in remote and underserved areas.

Detection of Diabetic Retinopathy Using Clahe Based Transfer Learning

Abstract: Diabetic Retinopathy(DR) has been a noticeable increase in the number of people diagnosed as blind in today’s day and age. Many of the individuals who were diagnosed as blind had long- term diabetes. As a result, persons who have had diabetes for a long time may develop diabetic retinopathy, an eye disease. This usually happens when high blood sugar levels damage the retina's blood vessels. These blood vessels have the potential to enlarge and leak. All of these variables have the potential to cause blindness. Diabetic Retinopathy affects roughly 117.12 million people, according to WHO (World Health Organization) figures from 2021 [Global Prevalence of DR and Projection of burden through 2045].Diabetic retinopathy is divided into four categories: no DR, moderate Nonproliferative (NP), Moderate NPDR, severe NPDR, and Proliferative DR. Early detection of DR can prevent blindness caused by long-term diabetes, which can be accomplished if DR is detected early. We employ a preprocessing approach called Contrast Limited Adaptive Histogram Equalization (CLAHE) and CNN algorithms like Alex net, Resnet, and Train net to classify eyeballs in this research. Normal eyes are classified as mild np, moderate np, and severe np based on the accuracy predicted. The evaluation results reveal that the suggested pre-processing algorithm CLAHE is effective in removing noise and redundant features from an input image, and when fed into the Efficient network, this system achieves a 96 percent accuracy

Diabetic Retinopathy and Eye Screening: Diabetic Patients Standpoint, Their Practice, and Barriers; A Cross-Sectional Study.

Diabetes mellites (DM) is one of the most common systemic disorders in Saudi Arabia and worldwide. Diabetic retinopathy (DR) is a potentially blinding ophthalmic consequence of uncontrolled DM. The early detection of DR leads to an earlier intervention, which might be sight-saving. Our aim in this cross-sectional study is to assess patients' knowledge and practices regarding DR, and to detect the barriers for eye screening and receiving a check-up from an ophthalmologist. The study included 386 diabetic patients. One hundred and thirty-one patients (33.9%) had T1DM and 188 (48.7%) had T2DM. Most of the diabetic patients (73.3%) know that they must have an eye check-up regardless of their blood sugar level. DM was agreed to affect the retina in 80.3% of the patients, 56% of patients agree that DM complications are always symptomatic, and 84.5% know that DM could affect their eyes. The fact that blindness is a complication of diabetic retinopathy was known by 65% of the diabetic patients. A better knowledge was detected among patients older than 50 years of age (54.9%) compared to those aged less than 35 years (40.9%), which was statistically significant (p = 0.030). Additionally, 61.2% of diabetic patients who were university graduates had a significantly better knowledge in comparison to 33.3% of illiterate patients (p = 0.006). Considering the barriers to not getting one's eyes screened earlier, a lack of knowledge was reported by 38.3% of the patients, followed by lack of access to eye care (24.4%). In conclusion, there is a remarkable increase in the awareness of DR among the Saudi population. This awareness might lead to an earlier detection and management of DR.

An efficient early detection of diabetic retinopathy using dwarf mongoose optimization based deep belief network

SummaryIn general, diabetic retinopathy (DR) is a common ocular disease that causes damage to the retina due to blood leakage from the vessels. Earlier detection of DR becomes a complicated task and it is necessary to prevent complete blindness. Various physical examinations are employed in DR detection but manual diagnosis results in misclassification results. Therefore, this article proposes a novel technique to predict and classify the DR disease effectively. The significant objective of the proposed approach involves the effective classification of fundus retinal images into two namely, normal (absence of DR) and abnormal (presence of DR). The proposed DR detection utilizes three vital phases namely, the data preprocessing, image augmentation, feature extraction, and classification. Initially, the image preprocessing is done to remove unwanted noises and to enhance images. Then, the preprocessed image is augmented to enhance the size and quality of the training images. This article proposes a novel modified Gaussian convolutional deep belief network based dwarf mongoose optimization algorithm for effective extraction and classification of retinal images. In this article, an ODIR‐2019 dataset is employed in detecting and classifying DR disease. Finally, the experimentation is carried out and the proposed approach achieved 97% of accuracy. This implies that our proposed approach effectively classifies the fundus retinal images.

Tortuosity of branch retinal artery is more associated with the genesis and progress of diabetic retinopathy.

ObjectiveThe purpose of this study is to investigate the potential of using the tortuosity of branch retinal artery as a more promising indicator for early detection and accurate assessment of diabetic retinopathy (DR).Design and methodThe diagnoses, consisting of whether DR or not as well as DR severity, were given by ophthalmologists upon the assessment of those fundus images from 495 diabetic patients. Meanwhile, benefiting from those good contrast and high optical resolution fundus images taken by confocal scanning laser ophthalmoscope, the branch arteries, branch veins, main arteries and main veins in retina can be segmented independently, and the tortuosity values of them were further extracted to investigate their potential correlations with DR genesis and progress based on one-way ANOVA test.ResultsFor both two comparisons, i.e., between non-DR group and DR group as well as among groups with different DR severity levels, larger tortuosity increments were always observed in retinal arteries and the increments in branch retinal vessels were even larger. Furthermore, it was newly found that branch arterial tortuosity was significantly associated with both DR genesis (p=0.030) and DR progress (p<0.001).ConclusionBased on this cohort study of 495 diabetic patients without DR and with different DR severity, the branch arterial tortuosity has been found to be more closely associated with DR genesis as well as DR progress. Therefore, the branch arterial tortuosity is expected to be a more direct and specific indicator for early detection of DR as well as accurate assessment of DR severity, which can further guide timely and rational management of DR to prevent from visual impairment or even blindness resulting from DR.

Towards Explainable Deep Neural Networks for the Automatic Detection of Diabetic Retinopathy

Diabetic Retinopathy (DR) is a common complication associated with diabetes, causing irreversible vision loss. Early detection of DR can be very helpful for clinical treatment. Ophthalmologists’ manual approach to DR diagnoses is expensive and time-consuming; thus, automatic detection of DR is becoming vital, especially with the increasing number of diabetes patients worldwide. Deep learning methods for analyzing medical images have recently become prevalent, achieving state-of-the-art results. Consequently, the need for interpretable deep learning has increased. Although it was demonstrated that the representation depth is beneficial for classification accuracy for DR diagnoses, model explainability is rarely analyzed. In this paper, we evaluated three state-of-the-art deep learning models to accelerate DR detection using the fundus images dataset. We have also proposed a novel explainability metric to leverage domain-based knowledge and validate the reasoning of a deep learning model’s decisions. We conducted two experiments to classify fundus images into normal and abnormal cases and to categorize the images according to the DR severity. The results show the superiority of the VGG-16 model in terms of accuracy, precision, and recall for both binary and DR five-stage classification. Although the achieved accuracy of all evaluated models demonstrates their capability to capture some lesion patterns in the relevant DR cases, the evaluation of the models in terms of their explainability using the Grad-CAM-based color visualization approach shows that the models are not necessarily able to detect DR related lesions to make the classification decision. Thus, more investigations are needed to improve the deep learning model’s explainability for medical diagnosis.

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.

Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean AUC of 99.15%.

Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model

Diabetic retinopathy (DR) is a diabetic ocular manifestation that leads to loss of visual impairment and blindness in people worldwide. Detecting and diagnosing the DR remains a major question in this task. This developed work uses a robust hybrid binocular Siamese with a deep learning approach to classify the DR image. Initially, a pre-processing stage is introduced to remove unwanted noises. To perform this cross guided bilateral filter (CGBF) approach is emphasized. After the pre-processing, the feature extraction stage is presented to extract the features from the processed image. A wavelet-based Chimp optimization algorithm (WBCOA) is established for the extraction of features. After feature extraction, segmentation of optical disc (OD) and blood vessel (BV) is done via open closed watershed management (OCWSM). For the classification, binocular Siamese based AlexNet and GoogleNet with the SVM model are proposed in this work. The segmented OD and BV are input to the proposed hybrid DL network. Finally, the extracted images are fused and classified using the Support Vector Machine (SVM) model. The proposed method is implemented in Python and tested on DIARETDB0 (DB0) and DIARETDB1 (DB1) datasets. The proposed hybrid DL network attained 94% and 94.83% accuracy on DB0 and DB1 datasets, respectively. Also, the proposed model’s outcomes are compared with various existing approaches. The proposed method conducted statistical analysis for the DR image based on mean and standard deviation (SD) to obtain an efficient output. These outcomes prove that the proposed hybrid DL system is accurate for early DR detection and deliver effective treatment of diabetes.

Semi-supervised Blindness Detection with Neural Network Ensemble

Diabetic retinopathy (DR), a common complication of diabetes mellitus, is a major cause of visual loss among the working-age population. Since DR vision loss is irreversible, early detection of DR is crucial for preventing vision loss in patients. However, manual detection of DR remains time costly and inefficient. In this paper, an ensemble of 6 pre-trained neural networks (including EfficientNets, ResNet, and Inception) are combined. The compatibility of different networks is tested by creating different combinations of networks and evaluating their relative performance. Pseudo-labelling is used to further increase accuracy. With a limited training data set of only 5592 images, the final neural network ensemble achieved an accuracy of 0.864.

Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection

ABSTRACT Diabetic retinopathy (DR) is a micro vascular problem caused by diabetes that can lead to loss of sight. The early detection of diabetic retinopathy is important to avoid the severity of sightlessness. In this manuscript, a comparative analysis of several deep learning methods for DR identification is proposed. The input fundus images are taken from a standard dataset pre-processed by the Mathematical Morphology process. Moreover, the images are segregated using a Multilevel segmentation of the Region of interest (ROI) based on the split and merge algorithm. After that, an original deep learning architecture is utilized to categorize the segregated fundus images. Deep learning methods, such as Convolution neural network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Fuzzy K-means cluster (FKM) and Discriminant Analysis (DA) are proposed to classify the DR. The proposed DR identification and detection with CNN provides 65.54% SP, 100% SE, 78.54% SV and 96.95% ACC. Finally, CNN shows better performance than other classifiers.

Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

ObjectiveEarly identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.MethodsFrom 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.ResultsThe mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.ConclusionsThe nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

Detection of Diabetic Retinopathy Using Deep Learning

Abstract: Diabetic retinopathy is one of the most dangerous complications of diabetes, leading to permanent blindness if left untreated. One of the major challenges is early detection, which is very important for the success of treatment. Unfortunately, accurate identification of the stage of diabetic retinopathy is notoriously tricky and requires expert human interpretation of fundus images. Simplifying the detection step is essential and can help millions of people. Convolutional Neural Networks (CNNs) have been successfully used in many neighboring subjects and for the diagnosis of diabetic retinopathy itself. However, the high cost of large annotated datasets as well as inconsistencies between different clinicians hinders the implementation of these methods. In this paper, we propose an automatic method based on deep learning to detect the stage of diabetic retinopathy using a single human fundus image. In addition, we propose a multi-stage transfer learning approach that uses similar datasets with different labels. The presented method can be used as a screening method for the early detection of diabetic retinopathy with a sensitivity and specificity of 0.99 and is ranked 54 out of 2943 competing methods (quadratic weighted kappa score 0.925466) on the APTOS 2019 Blindness Detection Dataset (13,000 images).

Comparison of central macular thickness between diabetic patients without clinical retinopathy and non-diabetic patients.

Diabetic retinopathy (DR) is one of the leading causes of blindness in patients between 20 and 60 years of age which can be prevented by early detection of diabetic retinopathy. The duration of diabetes is probably the strongest predictor for development and progression of retinopathy. Optical Coherence Tomography (OCT) is a recent advance in imaging which is sensitive in early detection of small changes in macular thickness. This hospital based cross-sectional study was done at a tertiary referral center in Kathmandu, Nepal where 364 eyes of 182 patients (182 eyes in 91 patients in each group diabetes without retinopathy group and nondiabetic group) were evaluated. Thickness of the macula was determined by using Spectral Domain Optical Coherence Tomography (SD- OCT) and compared between diabetic patients without clinical retinopathy and nondiabetic patients. The mean CMT as measured by Spectral Domain Optical Coherence Tomography in diabetic patients was 236.29±40.31 µm whereas it was 244.25±30.51 µm in non-diabetic cases. The mean central macular thickness of diabetic patients with duration of diabetes less than 1 year, 1-5 years, 6-10 years, 11-15 years and more than 15 years were 217.19±42.22, 233.49±45.69, 248.5±31.37, 250.89±21.62 and 240.75±11.26 respectively. This study concluded that in diabetic patients there was an initial decrease in central macular thickness which gradually increased with increasing duration of diabetes mellitus. Examination of macular thickness could be a useful modality to evaluate progression of disease before appearance of other clinical signs of diabetic retinopathy.

Exploring the Parametric Impact on a Deep Learning Model and proposal of a 2-Branch CNN for Diabetic Retinopathy Classification with Case Study in IoT-Blockchain based Smart Healthcare System

Smart healthcare has changed the way how the patient interacts with the specialists for treatment. However, security and support for various diseases are still the concern for such smart automated systems. One of the critical diseases namely Diabetic Retinopathy (DR), is a major concern for the person with prolonged diabetes and may lead to complete blindness irrespective of age groups. Moreover, in recent years blockchain has gained popularity in providing secure communication between sender and receiver. Hence, this work focus on designing a blockchain-based smart healthcare system for the early detection of diabetic retinopathy. However, early detection of DR impose complexities and requires expert diagnosis, which is not available everywhere. Hence, the proposed smart healthcare model contains a Computer-Aided Diagnosis (CAD) assistance for early detection of symptoms of the disease. The CAD model may assist the ophthalmologists in the early detection of DR, which requires intensive research in developing an efficient and accurate model that can operate without human interaction. Convolutional Neural Network (CNN) is one of the learning models in AI that has shown its potential in computer vision applications. However, the performance of CNN depends on several factors like the pooling approach, activation function, learning rate and CNN architecture. This study provides an empirical analysis of these factors to design the best model for early detection of DR. The best model can be used to develop IoT based smart devices to detect DR in diabetic patients. The study also explains the importance of IoT and blockchain-based technology for the development of smart healthcare systems. The values of the parameters and type of hyperparameters chosen from the study is used in a proposed 2-branch CNN model, and the model is validated using the Kaggle fundus image set. Analysis of various parameters and using their best values gives an outstanding performance in the proposed 2-branch CNN model.

Early Diabetic Retinopathy Detection Using Deep Learning Algorithms

Abstract: Diabetic Retinopathy (DR) is caused by the high blood glucose level which causes micro vascular complications in eyes which lead to vision loss. (MA)Microaneurysms formation in the retinal is sign of diabetic rent ropy which can be cured at early stage. Finding Microaneurysms (MA) presence in the eye image and recognition of diabetic retinopathy at early stage is difficult. Technology of deep learning makes it easier and efficient for analysis of eyes to detect MA presence which can be done by image detection and segmentation with good performance and accuracy using deep learning algorithms. This will help us to differentiated between affected retina and non-affected one. The given system can use deep convolution neural network for semantic segmentation of fundus images which increase efficiency and accuracy of NPDR (non- proliferated diabetic retinopathy) prediction. Keywords: PCA Principle Component Analysis DA Diabetic Retinopathy MA Microaneurysms NPDR Non proliferated diabetic retinopathy BPNN Back propagation neural network

Diabetic Retinopathy Detection Using Machine Learning

Diabetic retinopathy is a disorder induced by long-term diabetes that can result in total blindness if not addressed. As a result, early detection of diabetic retinopathy is critical, as is the medical treatment to prevent its adverse effects. Manual ophthalmologist detection takes longer and produces considerable discomfort during examination. Machine learning has recently become one of the most popular strategies for improving performance in a variety of sectors, including medical picture analysis and classification. As a result, an automated system aids in the early detection of diabetic retinopathy. Using a combination of neural networks, this research offers the extraction of exudates, haemorrhages, and micro-aneurysms and classification by machine learning.

Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science

Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. Our study focuses on the early detection of Diabetic Retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. Our dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, our research suggests that bagged trees and KNN are good classifiers for DR.