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Machine Learning as a Support for the Diagnosis of Type 2 Diabetes

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...

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Autores principales: Agliata, Antonio, Giordano, Deborah, Bardozzo, Francesco, Bottiglieri, Salvatore, Facchiano, Angelo, Tagliaferri, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095542/
https://www.ncbi.nlm.nih.gov/pubmed/37047748
http://dx.doi.org/10.3390/ijms24076775
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author Agliata, Antonio
Giordano, Deborah
Bardozzo, Francesco
Bottiglieri, Salvatore
Facchiano, Angelo
Tagliaferri, Roberto
author_facet Agliata, Antonio
Giordano, Deborah
Bardozzo, Francesco
Bottiglieri, Salvatore
Facchiano, Angelo
Tagliaferri, Roberto
author_sort Agliata, Antonio
collection PubMed
description 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.
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spelling pubmed-100955422023-04-13 Machine Learning as a Support for the Diagnosis of Type 2 Diabetes Agliata, Antonio Giordano, Deborah Bardozzo, Francesco Bottiglieri, Salvatore Facchiano, Angelo Tagliaferri, Roberto Int J Mol Sci Article 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. MDPI 2023-04-05 /pmc/articles/PMC10095542/ /pubmed/37047748 http://dx.doi.org/10.3390/ijms24076775 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/licenses/by/4.0/).
spellingShingle Article
Agliata, Antonio
Giordano, Deborah
Bardozzo, Francesco
Bottiglieri, Salvatore
Facchiano, Angelo
Tagliaferri, Roberto
Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title_full Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title_fullStr Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title_full_unstemmed Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title_short Machine Learning as a Support for the Diagnosis of Type 2 Diabetes
title_sort machine learning as a support for the diagnosis of type 2 diabetes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095542/
https://www.ncbi.nlm.nih.gov/pubmed/37047748
http://dx.doi.org/10.3390/ijms24076775
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