[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122070-en":3,"doc-seo-122070-105":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},122070,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis - read online free","Healthcare experts increasingly use machine learning to improve patient outcomes and reduce healthcare costs. Machine learning supports disease diagnosis, patient risk classification, individualized treatment suggestions, and drug development by learning patterns from electronic health records, medical images, and other data sources. This study compares logistic regression, Adaboost, and naïve Bayes for diabetes prediction using correct classification rate supported by confusion matrix analysis. The dataset comes from the Vanderbilt University repository, and all three algorithms show strong predictive effectiveness, with logistic regression and Adaboost exceeding 92% and naïve Bayes exceeding 90%.","A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis  \nMaadM. Mijwil*1, Mohammad Aljanabi2  \n1Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq. 2Department of Computer, College of Education, Aliraqia University, Baghdad, Iraq.  \n*Corresponding Author.  \nReceived 28/04/0202, Revised 20/06/0202, Accepted 22/06/0202, Published Online First 20/10/2023, Published 01/05/2024  \n © 2022 The Author(s) . Published by College of Science for Women, University of Baghdad.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract  \nHealthcare experts have been employing machine learning more and more in recent years to enhance patient outcomes and reduce costs. In addition, machine learning has been applied in various areas, including disease diagnosis, patient risk classification, customized treatment suggestions, and drug development. Machine learning algorithms can scrutinize vast quantities of data from electronic health records, medical images, and other sources to identify patterns and make predictions, which can support healthcare professionals and experts in making better-informed decisions, enhancing patient care, and determining a patient's health status. In this regard, the author opted to compare the performance of three algorithms (logistic regression, Adaboost, and naïve bayes) through the correct classification rate for diabetes prediction in order to ensure the effectiveness of accurate diagnosis. The dataset applied in this work is obtained from the Vanderbilt university institutional repository and is publicly available data. The study determined that three algorithms are very effective at prediction. Mainly, logistic regression and Adaboost had a classification rate above 92%, and the naive bayes algorithm achieved a classification rate above 90% .  \nKeywords: Algorithms, Classification, Confusion Matrix, Diabetes, Machine Learning.  \nIntroduction  \nDiabetes is a dangerous disease that occurs as a result of an imbalance between blood sugar and the hormone insulin 1,2 . As a result of this disease, it is not possible to use sugar as it should, so blood sugar begins to spin freely in the blood. After a period of time, damage to the blood circulation in the body may occur and the disease may become fatal in the long run because it will seek to destroy the blood vessels and organs Fig 13,4 . So, diabetes is a malignant disease and is usually observed after stability in the body. There are a set of symptoms,  \nincluding urinating more frequently than usual often at night, excessive thirst, sudden weight loss, severe hunger, lack of vision (blurred), skin problems (itchy skin), fatigue, and weakness. When insulin hormones are not secreted in a balanced manner, the body will make a serious effort to remove sugar from the blood, and thus the patient will feel exhausted and weak 5-7. Weakness is one of the major symptoms of diabetes, as the body constantly loses water in the cells and isin intense activity, which can lead to fainting 8-10. After that, the patient reaches the stage of continuous  \nand unreasonable depression, as well as tension, as this stage leads to the generation of sudden outbursts of anger, which is one of the most noticeable symptoms of diabetes. Moreover, there is another type of diabetes, known as hidden diabetes 11,12, where physicians and healthcare professionals define it as the first stage of diabetes, and the symptoms are unclear. Therefore, recognizing this disease at this stage and taking precautions will facilitate treatment and prevent the disease from developing and spreading in the body. The most notable signs of the onset of this stage and the most well-known are excess weig","cbCairhBeCkPyhxa","https://ap.wps.com/l/cbCairhBeCkPyhxa","pdf",1573937,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Diabetes definition and symptoms\n## Diabetes statistics and epidemiology\n## Risk factors and prevention approaches","[{\"question\":\"Which machine learning algorithms are compared for diabetes prediction in this study?\",\"answer\":\"The study compares logistic regression, Adaboost, and naïve Bayes for classifying diabetes using a confusion matrix-based evaluation.\"},{\"question\":\"What dataset is used for the experiments?\",\"answer\":\"The dataset is obtained from the Vanderbilt University institutional repository and is publicly available.\"},{\"question\":\"How do the algorithms perform in classification accuracy?\",\"answer\":\"Logistic regression and Adaboost achieve classification rates above 92%, while naïve Bayes reaches above 90% for diabetes prediction.\"}]","A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis - read online free | PDF",1785808678,43,{"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},"a-comparative-analysis-of-machine-learning-algorithms-for-classification-of-diabetes-utilizing-confusion-matrix-analysis-read-online-free","",{"@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/a-comparative-analysis-of-machine-learning-algorithms-for-classification-of-diabetes-utilizing-confusion-matrix-analysis-read-online-free/122070/",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","2026-08-04",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},"Which machine learning algorithms are compared for diabetes prediction in this study?","Question",{"text":75,"@type":76},"The study compares logistic regression, Adaboost, and naïve Bayes for classifying diabetes using a confusion matrix-based evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used for the experiments?",{"text":80,"@type":76},"The dataset is obtained from the Vanderbilt University institutional repository and is publicly available.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the algorithms perform in classification accuracy?",{"text":84,"@type":76},"Logistic regression and Adaboost achieve classification rates above 92%, while naïve Bayes reaches above 90% for diabetes prediction.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]