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Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms

Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast all over the world. Diabetic treatment is important to prevent the development of several complication...

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Autores principales: Alcalá-Rmz, Vanessa, Galván-Tejada, Carlos E., García-Hernández, Alejandra, Valladares-Salgado, Adan, Cruz, Miguel, Galván-Tejada, Jorge I., Celaya-Padilla, Jose M., Luna-Garcia, Huizilopoztli, Gamboa-Rosales, Hamurabi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8067355/
https://www.ncbi.nlm.nih.gov/pubmed/33917300
http://dx.doi.org/10.3390/healthcare9040422
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author Alcalá-Rmz, Vanessa
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Valladares-Salgado, Adan
Cruz, Miguel
Galván-Tejada, Jorge I.
Celaya-Padilla, Jose M.
Luna-Garcia, Huizilopoztli
Gamboa-Rosales, Hamurabi
author_facet Alcalá-Rmz, Vanessa
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Valladares-Salgado, Adan
Cruz, Miguel
Galván-Tejada, Jorge I.
Celaya-Padilla, Jose M.
Luna-Garcia, Huizilopoztli
Gamboa-Rosales, Hamurabi
author_sort Alcalá-Rmz, Vanessa
collection PubMed
description Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast all over the world. Diabetic treatment is important to prevent the development of several complications, also lipid profile monitoring is important. For that reason the aim of this work is the implementation of machine learning algorithms that are able to classify cases, that corresponds to patients diagnosed with diabetes that have diabetes treatment, and controls that refers to subjects who do not have diabetes treatment but some of them have diabetes, bases on lipids profile levels. Logistic regression, K-nearest neighbor, decision trees and random forest were implemented, all of them were evaluated with accuracy, sensitivity, specificity and AUC-ROC curve metrics. Artificial neural network obtain an acurracy of 0.685 and an AUC value of 0.750, logistic regression achieve an accuracy of 0.729 and an AUC value of 0.795, K-nearest neighbor gets an accuracy of 0.669 and an AUC value of 0.709, on the other hand, decision tree reached an accuracy pg 0.691 and a AUC value of 0.683, finally random forest achieve an accuracy of 0.704 and an AUC curve of 0.776. The performance of all models was statistically significant, but the best performance model for this problem corresponds to logistic regression.
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spelling pubmed-80673552021-04-25 Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms Alcalá-Rmz, Vanessa Galván-Tejada, Carlos E. García-Hernández, Alejandra Valladares-Salgado, Adan Cruz, Miguel Galván-Tejada, Jorge I. Celaya-Padilla, Jose M. Luna-Garcia, Huizilopoztli Gamboa-Rosales, Hamurabi Healthcare (Basel) Article Diabetes incidence has been a problem, because according with the World Health Organization and the International Diabetes Federation, the number of people with this disease is increasing very fast all over the world. Diabetic treatment is important to prevent the development of several complications, also lipid profile monitoring is important. For that reason the aim of this work is the implementation of machine learning algorithms that are able to classify cases, that corresponds to patients diagnosed with diabetes that have diabetes treatment, and controls that refers to subjects who do not have diabetes treatment but some of them have diabetes, bases on lipids profile levels. Logistic regression, K-nearest neighbor, decision trees and random forest were implemented, all of them were evaluated with accuracy, sensitivity, specificity and AUC-ROC curve metrics. Artificial neural network obtain an acurracy of 0.685 and an AUC value of 0.750, logistic regression achieve an accuracy of 0.729 and an AUC value of 0.795, K-nearest neighbor gets an accuracy of 0.669 and an AUC value of 0.709, on the other hand, decision tree reached an accuracy pg 0.691 and a AUC value of 0.683, finally random forest achieve an accuracy of 0.704 and an AUC curve of 0.776. The performance of all models was statistically significant, but the best performance model for this problem corresponds to logistic regression. MDPI 2021-04-06 /pmc/articles/PMC8067355/ /pubmed/33917300 http://dx.doi.org/10.3390/healthcare9040422 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Alcalá-Rmz, Vanessa
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Valladares-Salgado, Adan
Cruz, Miguel
Galván-Tejada, Jorge I.
Celaya-Padilla, Jose M.
Luna-Garcia, Huizilopoztli
Gamboa-Rosales, Hamurabi
Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title_full Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title_fullStr Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title_full_unstemmed Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title_short Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms
title_sort identification of people with diabetes treatment through lipids profile using machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8067355/
https://www.ncbi.nlm.nih.gov/pubmed/33917300
http://dx.doi.org/10.3390/healthcare9040422
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