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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8306487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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