[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117171-en":3,"doc-seo-117171-105":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},117171,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Solving Diabetes Diagnosis Problems Using Machine Learning","Research investigates how machine learning methods can address diabetes diagnosis challenges and improve clinical decision-making. Reported performance depends on data analysis approaches, chosen models, and the quality of provided data. Experiments use the Diabetes dataset with a Naive Bayes classifier and a linear-kernel SVM for binary classification. Models are trained after feature standardization and evaluated with confusion matrix and metrics including precision, recall, F1-measure, and AUC-ROC. Findings support better accuracy in diagnosing diabetes and classifying its type, enabling more individualized care plans based on patient-specific characteristics. The study also highlights prediction of complication risk and value of integrating data from multiple sources, supporting physicians and patients with informed choices.","Solving diabetes diagnosis problems using machine learning  \nDonaxon Olimboyeva 1, Davron Ziyadullaev2, *, Dilnoz Mukhamedieva2, Khosiyat Khujamkulova2, Mukhammadyahyo Teshaboyev3, and Gulchiroy Ziyodullaeva4  \n1“Alfraganus University” is a non-state higher education institution, Tashkent, Uzbekistan  \n2National Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute”, 100000 Tashkent, Uzbekistan  \n3Andijan State Medical Institute, 170100 Andijan, Uzbekistan  \n4Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, 100200 Tashkent, Uzbekistan  \nAbstract. This research is devoted to the study of the use of machine learning methods to solve the problem of diagnosing diabetes. The results of using machine learning in the context of diabetes are varied and depend on the methods of data analysis, the models used and the quality of the data provided. Experiments on the Diabetes dataset were conducted in the study using a Naive Bayes classifier model and a linear kernel SVM for a binary classification problem. Models are trained on the training dataset, standardizing features, and evaluated on the test set using confusion, precision, recall, F1-measure, and AUC-ROC metrics. The results obtained confirm that machine learning can improve the accuracy of diagnosing diabetes and classifying its type. This allows for customized treatment plans to be developed, considering the unique characteristics of each patient.  \nMachine learning models are also successful in predicting the likelihood of complications, allowing for preventative measures to be taken. Their use facilitates the integration of data from various sources, enriching patient information. In conclusion, machine learning-based decision support  \nsystems assist physicians and patients in making informed decisions.  \n1 Introduction  \nDiabetes is a chronic disease that affects millions of people around the world. For early diagnosis and effective management of diabetes, the medical community is turning to machine learning techniques and data analysis. The relevance of machine learning in the context of diabetes cannot be underestimated. This is a current research and practical area that has a number of important aspects. Diabetes is an increasingly common disease and its epidemic is growing steadily. Machine learning provides tools to more effectively diagnose and manage disease. Early diagnosis of diabetes and its type is key to successful treatment and prevention of complications. Machine learning makes it possible to develop models for early diagnosis. Each patient is unique, and machine learning can help create personalized  \n* Corresponding author: [dziyadullaev@inbox.ru](dziyadullaev@inbox.ru)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \ntreatment plans based on genetic, clinical, and laboratory data. Medical data includes a variety of sources and formats. Machine learning can integrate and analyze this data, creating a more complete picture of patients' conditions. Complications of diabetes can be dangerous. Machine learning models can predict the likelihood of their development and help to take measures to prevent them [1-4] .  \nThe Diabetes dataset is a valuable resource for researchers and practitioners in the fields of medical data analytics and machine learning. It enables the development and testing of models to predict and monitor the progression of diabetes and may contribute to improved approaches to the diagnosis and management of this serious chronic disease. The Diabetes dataset includes 10 numeric features that describe the patient’s conditions. These features represent important medical measurements and health characteristics. Below is a more detailed description of each of these feat","cbCaibem19gKevc2","https://ap.wps.com/l/cbCaibem19gKevc2","pdf",2351196,1,11,"English","en",105,"# Abstract\n# 1 Introduction\n## Diabetes and the need for early diagnosis\n## Diabetes dataset features and measurements","[{\"question\":\"Which machine learning models were used for diabetes diagnosis in this study?\",\"answer\":\"The study used a Naive Bayes classifier and a linear-kernel SVM to solve a binary classification problem for diabetes diagnosis.\"},{\"question\":\"How were the models evaluated?\",\"answer\":\"Models were trained on a training dataset with standardized features and evaluated on a test set using confusion matrix-related metrics including precision, recall, F1-measure, and AUC-ROC.\"},{\"question\":\"What additional benefits does machine learning provide beyond diagnosis?\",\"answer\":\"The approach can predict the likelihood of diabetes complications, enabling preventive measures, and helps integrate information from multiple data sources to enrich patient understanding.\"}]","Solving Diabetes Diagnosis Problems Using Machine Learning | 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machine learning models were used for diabetes diagnosis in this study?","Question",{"text":75,"@type":76},"The study used a Naive Bayes classifier and a linear-kernel SVM to solve a binary classification problem for diabetes diagnosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the models evaluated?",{"text":80,"@type":76},"Models were trained on a training dataset with standardized features and evaluated on a test set using confusion matrix-related metrics including precision, recall, F1-measure, and AUC-ROC.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional benefits does machine learning provide beyond diagnosis?",{"text":84,"@type":76},"The approach can predict the likelihood of diabetes complications, enabling preventive measures, and helps integrate information from multiple data sources to enrich patient 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