[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122229-en":3,"doc-seo-122229-105":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},122229,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques","Statistical and machine learning modelling techniques support description, prediction, and evaluation of epidemiological chronic diseases such as diabetes, which shows high global prevalence. The study models diabetes in Saudi Arabia using key risk factors—smoking, obesity, and physical inactivity—for adults aged 25 and above. Statistical and machine learning models are built to analyze incidence trends over 1999–2013 and to generate forecasts up to 2025 to inform health policy and resource allocation. Multiple Linear Regression, Support Vector Regression, Bayesian Linear Regression, ANFIS, and ANN are assessed with MSE, RMSE, MAPE, and R-squared, with ANFIS achieving the strongest performance.","algorithms   \nArticle  \nPrediction of Diabetes Using Statistical and Machine Learning Modelling Techniques  \nEntissar Almutairi *, Maysam Abbod  and Ziad Hunaiti *  \nAcademic Editors: Francesc Pozo and Frank Werner  \nReceived: 22 October 2024  \nRevised: 10 February 2025  \nAccepted: 26 February 2025  \nPublished: 5 March 2025  \nCitation: Almutairi, E.; Abbod, M.; Hunaiti, Z. Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques. Algorithms 2025, 18, 145. [https://](https://)[ ](https://)[doi.org/10.3390/a18030145](doi.org/10.3390/a18030145)  \n[Copyright:](Copyright:) © 2025 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://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nDepartment of Electronic and Electrical Engineering, Brunel University of London, Uxbridge UB8 3PH, UK; [maysam.abbod@brunel.ac.uk](maysam.abbod@brunel.ac.uk)  \n* Correspondence: [1416467@alumni.brunel.ac.uk](1416467@alumni.brunel.ac.uk) (E.A.); [ziad.hunaiti@brunel.ac.uk](ziad.hunaiti@brunel.ac.uk) (Z.H.)  \nAbstract: Statistical and machine learning modelling techniques have been effectively used in the healthcare domain and the prediction of epidemiological chronic diseases such as diabetes, which is classified as an epidemic due to its high rates of global prevalence. These techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This paper models diabetes disease in Saudi Arabia using the most relevant risk factors, namely smoking, obesity, and physical inactivity for adults aged ≥25 years. The aim of this study is based on developing statistical and machine learning models for the purpose of studying the trends in incidence rates of diabetes over 15 years (1999–2013) and to obtain predictions for future levels of the disease up to 2025, to support health policy planning and resource allocation for controlling diabetes. Different models were developed, namely Multiple Linear Regression (MLR), Support Vector Regression (SVR), Bayesian Linear Regression (BLM), Adaptive NeuroFuzzy Inference model (ANFIS), and Artificial Neural Network (ANN) . The performance of the developed models is evaluated using four statistical metrices: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R-squared. Based on the results, it can be observed that the overall performance for all proposed models was reasonably good; however, the best results were achieved by the ANFIS model with RMSE = 0.04 and R2 = 0.99 for men’straining data, and RMSE = 0.02 and R2 = 0.99 for women’s training data.  \nKeywords: machine learning; diabetes; regression; statistical metrices  \n1. Introduction  \nDiabetes is a serious health problem that is growing significantly around the world because of several demographic and behavioural factors, including increasing population density, urbanisation, an aging population, the prevalence of obesity, and low physical activity. Diabetes Mellitus (DM) is a group of metabolic disorders characterised by chronic hyperglycaemia due to deficiencies in insulin production, resistance to insulin, or both. This condition leads to abnormalities in the metabolism of carbohydrates, fats, and proteins, and, over time, it can result in complications affecting various organs, including the eyes, kidneys, nerves, heart, and blood vessels [1,2] . There are three types of diabetes classified according to aetiology and clinical picture: type 1 diabetes, type 2 diabetes, and gestational diabetes. Patients with type 1 diabetes need insulin injections to survive, while type 2 diabetes, which represents most cases, is a defect in the secretion and function of insulin, meaning some diabetics of this ","cbCaionGJKGFDd9a","https://ap.wps.com/l/cbCaionGJKGFDd9a","pdf",3229325,1,21,"English","en",105,"# Introduction\n## Diabetes background and classification\n## Global burden and Saudi Arabia context\n## Prediction methods and machine learning in public health\n# Study contribution","[{\"question\":\"Which risk factors are used to model diabetes in the study?\",\"answer\":\"The model uses smoking, obesity, and physical inactivity as the main risk factors for adults aged 25 and above.\"},{\"question\":\"What time range is analyzed for diabetes incidence and what forecasts are produced?\",\"answer\":\"Incidence trends are studied over 15 years from 1999 to 2013, and predictions are generated for future levels up to 2025.\"},{\"question\":\"Which modelling approach delivers the best reported results?\",\"answer\":\"ANFIS shows the best performance, with RMSE and R-squared values reported as 0.04 and 0.99 for men and 0.02 and 0.99 for women training data.\"}]","Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques | 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risk factors are used to model diabetes in the study?","Question",{"text":75,"@type":76},"The model uses smoking, obesity, and physical inactivity as the main risk factors for adults aged 25 and above.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What time range is analyzed for diabetes incidence and what forecasts are produced?",{"text":80,"@type":76},"Incidence trends are studied over 15 years from 1999 to 2013, and predictions are generated for future levels up to 2025.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modelling approach delivers the best reported results?",{"text":84,"@type":76},"ANFIS shows the best performance, with RMSE and R-squared values reported as 0.04 and 0.99 for men and 0.02 and 0.99 for women training 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