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Machine learning and deep learning predictive models for type 2 diabetes: a systematic review
Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686642/ https://www.ncbi.nlm.nih.gov/pubmed/34930452 http://dx.doi.org/10.1186/s13098-021-00767-9 |
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author | Fregoso-Aparicio, Luis Noguez, Julieta Montesinos, Luis García-García, José A. |
author_facet | Fregoso-Aparicio, Luis Noguez, Julieta Montesinos, Luis García-García, José A. |
author_sort | Fregoso-Aparicio, Luis |
collection | PubMed |
description | Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models. |
format | Online Article Text |
id | pubmed-8686642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86866422021-12-21 Machine learning and deep learning predictive models for type 2 diabetes: a systematic review Fregoso-Aparicio, Luis Noguez, Julieta Montesinos, Luis García-García, José A. Diabetol Metab Syndr Review Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models. BioMed Central 2021-12-20 /pmc/articles/PMC8686642/ /pubmed/34930452 http://dx.doi.org/10.1186/s13098-021-00767-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Fregoso-Aparicio, Luis Noguez, Julieta Montesinos, Luis García-García, José A. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title | Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title_full | Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title_fullStr | Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title_full_unstemmed | Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title_short | Machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
title_sort | machine learning and deep learning predictive models for type 2 diabetes: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686642/ https://www.ncbi.nlm.nih.gov/pubmed/34930452 http://dx.doi.org/10.1186/s13098-021-00767-9 |
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