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Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes
Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML model...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378239/ https://www.ncbi.nlm.nih.gov/pubmed/37510127 http://dx.doi.org/10.3390/diagnostics13142383 |
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author | Iparraguirre-Villanueva, Orlando Espinola-Linares, Karina Flores Castañeda, Rosalynn Ornella Cabanillas-Carbonell, Michael |
author_facet | Iparraguirre-Villanueva, Orlando Espinola-Linares, Karina Flores Castañeda, Rosalynn Ornella Cabanillas-Carbonell, Michael |
author_sort | Iparraguirre-Villanueva, Orlando |
collection | PubMed |
description | Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising. |
format | Online Article Text |
id | pubmed-10378239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103782392023-07-29 Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes Iparraguirre-Villanueva, Orlando Espinola-Linares, Karina Flores Castañeda, Rosalynn Ornella Cabanillas-Carbonell, Michael Diagnostics (Basel) Article Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising. MDPI 2023-07-15 /pmc/articles/PMC10378239/ /pubmed/37510127 http://dx.doi.org/10.3390/diagnostics13142383 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 Iparraguirre-Villanueva, Orlando Espinola-Linares, Karina Flores Castañeda, Rosalynn Ornella Cabanillas-Carbonell, Michael Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title | Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title_full | Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title_fullStr | Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title_full_unstemmed | Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title_short | Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes |
title_sort | application of machine learning models for early detection and accurate classification of type 2 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378239/ https://www.ncbi.nlm.nih.gov/pubmed/37510127 http://dx.doi.org/10.3390/diagnostics13142383 |
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