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Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take th...

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Detalles Bibliográficos
Autores principales: Joshi, Ram D., Dhakal, Chandra K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306487/
https://www.ncbi.nlm.nih.gov/pubmed/34299797
http://dx.doi.org/10.3390/ijerph18147346
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author Joshi, Ram D.
Dhakal, Chandra K.
author_facet Joshi, Ram D.
Dhakal, Chandra K.
author_sort Joshi, Ram D.
collection PubMed
description Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.
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spelling pubmed-83064872021-07-25 Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches Joshi, Ram D. Dhakal, Chandra K. Int J Environ Res Public Health Article Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs. MDPI 2021-07-09 /pmc/articles/PMC8306487/ /pubmed/34299797 http://dx.doi.org/10.3390/ijerph18147346 Text en © 2021 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
Joshi, Ram D.
Dhakal, Chandra K.
Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title_full Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title_fullStr Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title_full_unstemmed Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title_short Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
title_sort predicting type 2 diabetes using logistic regression and machine learning approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306487/
https://www.ncbi.nlm.nih.gov/pubmed/34299797
http://dx.doi.org/10.3390/ijerph18147346
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