[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117757-en":3,"doc-seo-117757-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117757,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning Methods for Diabetes Prevalence Classification in Saudi Arabia","Machine learning algorithms are applied to the prediction and evaluation of chronic epidemiological diseases, including diabetes mellitus, a condition with high global prevalence. This study evaluates classification methods to identify diabetes prevalence rates and forecast trend patterns in Saudi Arabia using behavioural risk factors: smoking, obesity, and inactivity. Models are built with algorithms including linear discriminant, support vector machines, K-nearest neighbour variants, and neural network pattern recognition, with kernel-function and KNN-type choices tuned. Performance is assessed by accuracy, prediction speed, and training time via MATLAB’s Classification Learner app, showing weighted KNN with the highest average accuracy of 94.5% and lower training time for both men and women datasets.","Article  \nMachine Learning Methods for Diabetes Prevalence Classification in Saudi Arabia  \nEntissar S. Almutairi and Maysam F. Abbod *  \nCitation: Almutairi, E.S.; Abbod, M.F. Machine Learning Methods for Diabetes Prevalence Classification in Saudi Arabia. Modelling 2023, 4, 37–55. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)modelling4010004  \nAcademic Editor: Alfredo Cuzzocrea  \nReceived: 6 December 2022  \nRevised: 20 January 2023  \nAccepted: 23 January 2023  \nPublished: 25 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Electronics and Electrical Engineering, Brunel University London, London UB8 3PH, UK  \n* [Correspondence: maysam.abbod@brunel.ac.uk](Correspondence: maysam.abbod@brunel.ac.uk)  \nAbstract: Machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, such as diabetes mellitus, which is classified as an epi-demic due to its high rates of global prevalence. Machine learning techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This study investigates the ability of different classification methods to classify diabetes prevalence rates and the predicted trends in the disease according to associated behavioural risk factors (smoking, obesity, and inactivity) in Saudi Arabia. Classification models for diabetes prevalence were developed using different machine learning algorithms, including linear discriminant (LD), support vector machine (SVM), K-nearest neighbour (KNN), and neural network pattern recognition (NPR) . Four kernel functions of SVM and two types of KNN algorithms were used, namely linear SVM, Gaussian SVM, quadratic SVM, cubic SVM, fine KNN, and weighted KNN. The performance evaluation in terms of the accuracy of each developed model was determined, and the developed classifiers were compared using the Classification Learner App in MATLAB, according to prediction speed and training time. The experimental results on the predictive performance analysis of the classification models showed that weighted KNN performed well in the prediction of diabetes prevalence rate, with the highest average accuracy of 94.5% and less training time than the other classification methods, for both men and women datasets.  \nKeywords: machine learning; diabetes; classification  \n1. Introduction  \nDiabetes mellitus (DM) can be defined as “a group of metabolic diseases characterised by hyperglycaemia resulting from defects in insulin secretion, insulin action or both”. DM-related disturbances in carbohydrate, fat, and protein metabolism are linked to chronic hyperglycaemia, and can result in long-term damage, dysfunction, and failure of various organs, especially the heart, eyes, kidneys, blood vessels, and nerves [1] .  \nThere are three types of DM classified according to aetiology and clinical picture: type 1 diabetes (T1DM), type 2 diabetes (T2DM), and gestational diabetes. T1DM is usually a result of absolute insulin deficiency due to the destruction of 􀀌 cells in the pancreas, mostly due to a cellular-mediated autoimmune process. T2DM is caused by insulin resistance and relative insulin deficiency. Gestational diabetes is recognised or first starts during pregnancy, which is characterised by glucose intolerance of varying degrees of severity [2] .  \nT2DM is the most common variant, accounting for 90% of diabetic diagnoses. People with T2DM are usually diagnosed after the age of 40, but younger adults or even children can be affected by this type. The symptoms of this type may not appear in the affected person for many years, and m","cbCaihbRJGm7Iflr","https://ap.wps.com/l/cbCaihbRJGm7Iflr","pdf",2768444,1,19,"English","en",105,"# Introduction\n## Diabetes mellitus overview and types\n## Risk factors and importance of prevalence modelling\n# Methods (overview)\n## Data and features: behavioural risk factors\n## Classification algorithms and model setup\n# Results and evaluation\n## Accuracy, prediction speed, and training time\n## Comparison of classifier performance\n# Conclusion","[{\"question\":\"Which machine learning algorithms are used for diabetes prevalence classification in the study?\",\"answer\":\"The study develops models using linear discriminant (LD), support vector machine (SVM), K-nearest neighbour (KNN), and neural network pattern recognition (NPR). It also tests multiple SVM kernels and two KNN types.\"},{\"question\":\"What risk factors are included to predict diabetes prevalence trends?\",\"answer\":\"The models use behavioural risk factors including smoking, obesity, and physical inactivity to characterize associated patterns influencing diabetes prevalence.\"},{\"question\":\"Which classifier performs best and how is performance measured?\",\"answer\":\"Weighted KNN performs best, achieving the highest average accuracy of 94.5% with less training time than other methods. Models are evaluated using accuracy, prediction speed, and training time using MATLAB’s Classification Learner app.\"}]","Machine Learning Methods for Diabetes Prevalence Classification in Saudi Arabia | PDF",1785679415,48,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-methods-for-diabetes-prevalence-classification-in-saudi-arabia","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-methods-for-diabetes-prevalence-classification-in-saudi-arabia/117757/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning algorithms are used for diabetes prevalence classification in the study?","Question",{"text":76,"@type":77},"The study develops models using linear discriminant (LD), support vector machine (SVM), K-nearest neighbour (KNN), and neural network pattern recognition (NPR). It also tests multiple SVM kernels and two KNN types.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What risk factors are included to predict diabetes prevalence trends?",{"text":81,"@type":77},"The models use behavioural risk factors including smoking, obesity, and physical inactivity to characterize associated patterns influencing diabetes prevalence.",{"name":83,"@type":74,"acceptedAnswer":84},"Which classifier performs best and how is performance measured?",{"text":85,"@type":77},"Weighted KNN performs best, achieving the highest average accuracy of 94.5% with less training time than other methods. 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