[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118487-en":3,"doc-seo-118487-105":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":4,"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},118487,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Diabetic Type Classification using Supervised Machine Learning Approaches","Diabetic retinopathy (DR) is a major global cause of blindness and requires timely lesion diagnosis and categorization to prevent irreversible vision loss. This work classifies diabetic versus non-diabetic cases using supervised machine learning models, training and evaluating algorithms including Decision Tree, ANN, KNN, SVM, Random Forest, and Gradient Boosting. The study uses public diabetes datasets with preprocessing, reporting that SVM and ANN exceed 80% accuracy, supporting their value for diabetes type classification and computer-aided diagnostic support.","Diabetic Type Classification using Supervised Machine  \nLearning Approaches  \nSARITAKUMARI1, DR. AMRITAUPADHAYA2  \n1Research Scholar, Department of Computer Science, Banasthali Vidyapith, Jaipur, Rajasthan, INDIA  \nEmail: [saritanaveenkaliraman@gmail.com](saritanaveenkaliraman@gmail.com)  \n2Assistant Professor, Department of Computer Science, Banasthali Vidyapith, Jaipur, Rajasthan, INDIA  \nEmail: [saiamrita27@gmail.com](saiamrita27@gmail.com)  \nKEYWORDS  \nDiabetes, SVM, Decision Trees, KNN, ANN, Gradient, Naive Bayes, Random Forest  \nABSTRACT:  \nDiabetic Retinopathy (DR) is the leading cause of blindness worldwide and a serious diabetic complication. To prevent vision loss, DR lesion diagnosis and categorization must be done early. DR early detection and treatment can significantly reduce the risk of vision loss. This paper focuses on classifying a sample into diabetic and non-diabetic using a variety of techniques, including Decision Tree, ANN, KNN, SVM, Random Forest, and Gradient Boosting Algorithms. The NCSU Diabetes the data set is preprocessed, and examples are trained and evaluated for accuracy; SVM and ANN achieve over 80% accuracy, demonstrating their potential in diabetes type classification. The PIMA Indians Dataset is used as a reference. The DR's manual diagnosing procedure Ophthalmologists' retina fundus scans take a lot of time, effort, money, and are prone to in contrast to computer-aided diagnosis systems, tomisdiagnosis. Machine learning has recently been one of the most widely used methods that has improved performance in several categories, for example. The best classifier for diabetic retinopathy is determined by SVM, Decision. This compares ANN classifiers, Tree, Logistic Regression, and k-Nearest Neighbors paper. Additionally, a study of the existing DR datasets has been conducted. Numerous difficult also covered are topics that need further research. The results of comparing various machine learning algorithms with earlier studies are favourable. This research enhances the diagnosis of diabetic retinopathy by demonstrating the effectiveness of several machine learning classifiers and assisting in the creation of precise and effective computer-aided diagnostic tools for management and early detection.  \n1. INTRODUCTION  \nDiabetes is one of the most prevalent lifestyle-related health conditions worldwide, with less than halfa billion people currently living with it. The number of people diagnosed with diabetes is expected to continue rising year after year. Type 2 diabetes is the most common form and typically affects older adults who lead sedentary lifestyles and are overweight. Diabetes occurs when the pancreas fails to produce enough insulin or when the body’s cells and tissues become resistant to insulin.  \nDiabetes mellitus is classified into three main types:  \n1. Type 1 Diabetes Mellitus: This type is characterized by the pancreas producing insufficient insulin, a condition also known as Insulin-Dependent Diabetes Mellitus (IDDM) . People with type 1 diabetes require daily insulin injections to manage their condition. Early intervention is crucial to prevent complications like diabetic retinopathy, a condition that can lead to blindness. Identifying diabetic retinopathy early can be challenging in many parts of Asia and Africa due to limited access to healthcare. Researchers are exploring artificial intelligence (AI) models that use machine learning to analyze retina fundus images, helping medical professionals detect and classify the stages of diabetic retinopathy, such as Normal, Moderate, and Proliferative Diabetic Retinopathy (PDR) .  \n2. Type 2 Diabetes Mellitus: In this form of diabetes, the body becomes resistant to insulin, meaning that the cells do not respond to insulin in the usual manner. Type 2 diabetes, also known  \nas Non-Insulin Dependent Diabetes Mellitus (NIDDM) or Adult-Onset Diabetes, is more common in people who are overweight or lead inactive lifestyles. This is the most prev","cbCaiivQkfOWYKWD","https://ap.wps.com/l/cbCaiivQkfOWYKWD","pdf",515368,1,11,"English","en",105,"# Introduction\n## Diabetes overview and types\n## Type 1 Diabetes Mellitus\n## Type 2 Diabetes Mellitus\n## Gestational Diabetes\n## Risk factors and health impacts","[{\"question\":\"What problem does the paper address in diabetes care?\",\"answer\":\"It targets early detection and categorization of diabetic cases, with focus on diabetic retinopathy to reduce the risk of vision loss through timely diagnosis.\"},{\"question\":\"Which supervised machine learning algorithms are used for classification?\",\"answer\":\"The study evaluates Decision Tree, ANN, KNN, SVM, Random Forest, and Gradient Boosting using a diabetes dataset workflow with preprocessing, training, and accuracy evaluation.\"},{\"question\":\"What results are reported for the model performance?\",\"answer\":\"SVM and ANN achieve over 80% accuracy, indicating strong potential for diabetic type classification and support for computer-aided diagnosis.\"}]","Diabetic Type Classification using Supervised Machine Learning Approaches | 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problem does the paper address in diabetes care?","Question",{"text":75,"@type":76},"It targets early detection and categorization of diabetic cases, with focus on diabetic retinopathy to reduce the risk of vision loss through timely diagnosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised machine learning algorithms are used for classification?",{"text":80,"@type":76},"The study evaluates Decision Tree, ANN, KNN, SVM, Random Forest, and Gradient Boosting using a diabetes dataset workflow with preprocessing, training, and accuracy evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"What results are reported for the model performance?",{"text":84,"@type":76},"SVM and ANN achieve over 80% accuracy, indicating strong potential for diabetic type classification and support for computer-aided 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