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Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening
Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983182/ https://www.ncbi.nlm.nih.gov/pubmed/35392258 http://dx.doi.org/10.1155/2022/8114049 |
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author | Cardozo, Glauco Pintarelli, Guilherme Brasil Andreis, Guilherme Rettore Lopes, Annelise Correa Wengerkievicz Marques, Jefferson Luiz Brum |
author_facet | Cardozo, Glauco Pintarelli, Guilherme Brasil Andreis, Guilherme Rettore Lopes, Annelise Correa Wengerkievicz Marques, Jefferson Luiz Brum |
author_sort | Cardozo, Glauco |
collection | PubMed |
description | Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing. |
format | Online Article Text |
id | pubmed-8983182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89831822022-04-06 Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening Cardozo, Glauco Pintarelli, Guilherme Brasil Andreis, Guilherme Rettore Lopes, Annelise Correa Wengerkievicz Marques, Jefferson Luiz Brum Biomed Res Int Research Article Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing. Hindawi 2022-03-29 /pmc/articles/PMC8983182/ /pubmed/35392258 http://dx.doi.org/10.1155/2022/8114049 Text en Copyright © 2022 Glauco Cardozo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Cardozo, Glauco Pintarelli, Guilherme Brasil Andreis, Guilherme Rettore Lopes, Annelise Correa Wengerkievicz Marques, Jefferson Luiz Brum Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title | Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title_full | Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title_fullStr | Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title_full_unstemmed | Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title_short | Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening |
title_sort | use of machine learning and routine laboratory tests for diabetes mellitus screening |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983182/ https://www.ncbi.nlm.nih.gov/pubmed/35392258 http://dx.doi.org/10.1155/2022/8114049 |
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