[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122675-en":3,"doc-seo-122675-105":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},122675,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","Machine Learning as a Support for the Diagnosis of Type 2 Diabetes","Diabetes is a chronic metabolic disorder marked by elevated blood glucose, and type 2 diabetes is the most prevalent form. Early diagnosis and timely intervention can limit the development of long-term complications. This study applies machine learning—particularly an artificial neural network—as decision-support for predicting onset and estimating individual risk using nonlinear patterns. A binary classifier is trained on combined datasets (NHANES, MIMIC-III, MIMIC-IV), yielding high performance with up to 86% accuracy and ROC AUC of 0.934, while highlighting the need for validation using longitudinal multi-measurement data.","Article  \nMachine Learning as a Support for the Diagnosis of Type  \n2 Diabetes  \nAntonio Agliata 1,2, Deborah Giordano 3, Francesco Bardozzo 1, Salvatore Bottiglieri 2, Angelo Facchiano 3, * and Roberto Tagliaferri 1  \nCitation: Agliata, A.; Giordano, D.; Bardozzo, F.; Bottiglieri, S.; Facchiano, A.; Tagliaferri, R. Machine Learning as a Support for the Diagnosis of Type 2 Diabetes. Int. J. Mol. Sci. 2023, 24, 6775. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijms24076775  \nAcademic Editors: Maria Vittoria Cubellis and Anna Marabotti  \nReceived: 15 February 2023  \nRevised: 31 March 2023  \nAccepted: 3 April 2023  \nPublished: 5 April 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/)) .  \n1 Dipartimento di Scienze Aziendali, Management and Innovation Systems, Universit degli Studi di Salerno, 84084 Fisciano, Italy  \n2 BC Soft, Centro Direzionale, Via Taddeo da Sessa Isola F10, 80143 Napoli, Italy  \n3 National Research Council, Institute of Food Science, Via Roma 64, 83100 Avellino, Italy  \n* Correspondence: [angelo.facchiano@isa.cnr.it](angelo.facchiano@isa.cnr.it)  \nAbstract: Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual's health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual's risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics' (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934 . Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.  \nKeywords: T2DM; neural network; artificial intelligence  \n1. Introduction  \nDiabetes is a chronic, metabolic disorder characterized by high blood sugar levels, determined by insufficient production or function of insulin, a hormone produced by the pancreas, which regulates the uptake and metabolism of glucose, the main source of energy for the body's cells.  \nThis pathology can be classified into three specific categories: type 1 diabetes (T1DM), type 2 diabetes (T2DM), and gestational diabetes mellitus (GDM), related to different causes. In T1DM, also known as juvenile diabetes or insulin-dependent diabetes, an autoimmune mechanism destroys the insulin-producing cells in the pancreas with a complete lack of insulin production. In T2DM, the most common form and often associated with obesity and a sedentary lifestyle, multifactorial causes (such as genetic and environmental factors) induce re","cbCaie42fk5Bf6aV","https://ap.wps.com/l/cbCaie42fk5Bf6aV","pdf",1858790,1,14,"English","en",105,"# Introduction\n## Diabetes overview and types\n## Motivation for early diagnosis\n## Role of artificial intelligence\n# Methods\n## Data sources and dataset construction\n## Model approach and evaluation\n# Results\n## Predictive performance metrics\n# Discussion\n## Reliability considerations and future work","[{\"question\":\"How does the study use machine learning for type 2 diabetes diagnosis support?\",\"answer\":\"It trains a binary classifier with machine learning to identify nonlinear relationships between patient measurements and type 2 diabetes onset, aiming to predict onset or estimate risk levels.\"},{\"question\":\"Which datasets are used in the research?\",\"answer\":\"The study uses three datasets: NHANES, MIMIC-III, and MIMIC-IV, which are combined into a single balanced dataset containing equal numbers of individuals with and without type 2 diabetes.\"},{\"question\":\"What performance did the model achieve?\",\"answer\":\"Using accuracy as the primary evaluation metric, the model reaches up to 86% accuracy and a ROC AUC value of 0.934.\"}]","Machine Learning as a Support for the Diagnosis of Type 2 Diabetes | 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does the study use machine learning for type 2 diabetes diagnosis support?","Question",{"text":75,"@type":76},"It trains a binary classifier with machine learning to identify nonlinear relationships between patient measurements and type 2 diabetes onset, aiming to predict onset or estimate risk levels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets are used in the research?",{"text":80,"@type":76},"The study uses three datasets: NHANES, MIMIC-III, and MIMIC-IV, which are combined into a single balanced dataset containing equal numbers of individuals with and without type 2 diabetes.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the model achieve?",{"text":84,"@type":76},"Using accuracy as the primary evaluation metric, the model reaches up to 86% accuracy and a ROC AUC value of 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