[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119521-en":3,"doc-seo-119521-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119521,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Performance Analysis of Machine Learning Techniques for Diabetic Retinopathy Detection - Paper","Diabetic retinopathy is an eye disease affecting the retinal tissue and often progresses without early warning signs, later becoming difficult to treat and potentially leading to partial or complete vision loss. To enable early identification, computer-aided systems using machine learning are developed for retinal image analysis. The work comparatively evaluates widely used techniques—including SVM, KNN, decision tree, random forest, logistic regression, naive Bayes, neural networks, and deep learning models like CNN, VGG16, ResNet50, EfficientNetB0, and InceptionV3—across five datasets, using AUC, classification accuracy, F1-score, precision, and recall.","Performance Analysis of Machine Learning Techniques for Diabetic Retinopathy Detection  \n1 Rachna Kumari, 2 Sanjeev Kumar, 3 Sunila Godara  \n1 Research Scholar, Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar, India.  \n2 Professor, Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar, India  \n3 Professor, Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology, Hisar, India  \n(Received: 02 September 2023 Revised: 14 October Accepted: 07 November)  \n\n| KEYWORDS\u003Cbr>Area Under Curve, Classification Accuracy, Diabetic Retinopathy. | ABSTRACT:\u003Cbr>Diabetic retinopathy is an eye disease that affects the light sensitive area of retina. It does not give any sign at initially but later it become very difficult to cure it. So it is very essential to detect it at initial stage for this various computer aided software is designed using machine learning techniques. This paper analyzed the most widely used machine learning techniques used for this disease detection i.e. SVM, KNN, decision tree, random forest, logistic regression neural network, naive bayes and deep learning architecture i.e. CNN, VGG16, ResNet50, EfficientNetB0, InceptionV3, CNN and SVM . Performance of these techniques is analyzed using five different datasets. AUC, CA, F1 score, precision, and recall are used for evaluation purpose. |\n| --- | --- |\n\nIntroduction  \nDiabetes is a long life disease that occurs due to high ratio of glucose in blood. If a person have this disease than he/she will be at high risk of many other disease e.g. kidney damage, neuropathy retinopathy, hearing impairment etc. diabetic retinopathy is one of these disease that occur due the diabetes. Diabetic retinopathy does not give any sign or symptom initially but after  \nsome time it become very dangerous and can lead to partial or even complete vision loss. And according to a survey upto 25000 thousand Indian loss their eye sight due to these diseases [1] . DR affects the light sensitive area of retina. Fig 1. Shows the five stages of DR. For identifying DR affects at different levels this disease is categorize in four stages mild, moderate, severe and proliferate stage.  \nFig. 1 Stages of Diabetic retinopathy  \nEach stage has different signs and symptoms with the help of which experts identify the stage of disease so that proper treatment can be given to patient at appropriate time. Table no. 1 shows the identification signs of all stages of DR.  \nTable 1: DR pathologies and their indication  \n\n| Stage of\u003Cbr>Diabetic Retinopathy | Pathologies | Identification |\n| --- | --- | --- |\n| No_DR | Normal retinal image |  |\n| Mild | Microneurisms | Bulges Red\u003Cbr>spots on Border of Retina |\n| Moderate | Haemorrhages,\u003Cbr>hard and soft exudates | White/ Yellowish Patches on Retina |\n| Severe | Haemorrhages,\u003Cbr>hard and soft exudates and MA in all quadrants | Red patches\u003Cbr>Deposits on Retina |\n| Proliferate | Haemorrhages,\u003Cbr>hard and soft exudates, Vitreous haemorrhages, New bloodvessel formation | Dark Yellowish\u003Cbr>patches on retina |\n\nWith the help of these signs and symptoms various machine learning techniques are used to classify this disease so that proper treatment can be given to patient at proper time. This paper provides a comparative analysis of various machine learning techniques that are widely used for detecting this disease. Main contribution of this paper is :  \n• This paper helps in analyzing existing DR detection techniques in a more appropriate way.  \n• It provides an overview of the classification techniques that are used in this field.  \n• This paper helps researches in choosing appropriate technique for building new model.  \nThis paper is divided into following subsections. First section is an introductory part of diabetic retinopathy and its stages. Second section provides to an overview of work that was done in this field. Third section des","cbCaic7ge1QTXUwj","https://ap.wps.com/l/cbCaic7ge1QTXUwj","pdf",497025,1,12,"English","en",105,"# Introduction\n## Related Work\n## Material and Methods\n## Results\n## Conclusion and Future Scope","[{\"question\":\"Why is early detection of diabetic retinopathy important?\",\"answer\":\"Diabetic retinopathy shows few or no symptoms initially, but later becomes dangerous and can cause partial or complete vision loss. Early detection supports timely and appropriate treatment.\"},{\"question\":\"Which machine learning and deep learning techniques are compared in the paper?\",\"answer\":\"The paper analyzes SVM, KNN, decision tree, random forest, logistic regression, naive Bayes, neural network approaches, and deep learning architectures including CNN, VGG16, ResNet50, EfficientNetB0, and InceptionV3.\"},{\"question\":\"How is the performance of the models evaluated?\",\"answer\":\"Model performance is assessed using AUC, classification accuracy (CA), F1 score, precision, and recall across five different datasets.\"}]","Performance Analysis of Machine Learning Techniques for Diabetic Retinopathy Detection - Paper | PDF",1785724751,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"performance-analysis-of-machine-learning-techniques-for-diabetic-retinopathy-detection-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/performance-analysis-of-machine-learning-techniques-for-diabetic-retinopathy-detection-paper/119521/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early detection of diabetic retinopathy important?","Question",{"text":75,"@type":76},"Diabetic retinopathy shows few or no symptoms initially, but later becomes dangerous and can cause partial or complete vision loss. Early detection supports timely and appropriate treatment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and deep learning techniques are compared in the paper?",{"text":80,"@type":76},"The paper analyzes SVM, KNN, decision tree, random forest, logistic regression, naive Bayes, neural network approaches, and deep learning architectures including CNN, VGG16, ResNet50, EfficientNetB0, and InceptionV3.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the performance of the models evaluated?",{"text":84,"@type":76},"Model performance is assessed using AUC, classification accuracy (CA), F1 score, precision, and recall across five different datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]