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Artificial intelligence with temporal features outperforms machine learning in predicting diabetes

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough t...

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Autores principales: Naveed, Iqra, Kaleem, Muhammad Farhat, Keshavjee, Karim, Guergachi, Aziz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599553/
https://www.ncbi.nlm.nih.gov/pubmed/37878561
http://dx.doi.org/10.1371/journal.pdig.0000354
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author Naveed, Iqra
Kaleem, Muhammad Farhat
Keshavjee, Karim
Guergachi, Aziz
author_facet Naveed, Iqra
Kaleem, Muhammad Farhat
Keshavjee, Karim
Guergachi, Aziz
author_sort Naveed, Iqra
collection PubMed
description Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.
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spelling pubmed-105995532023-10-26 Artificial intelligence with temporal features outperforms machine learning in predicting diabetes Naveed, Iqra Kaleem, Muhammad Farhat Keshavjee, Karim Guergachi, Aziz PLOS Digit Health Research Article Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities. Public Library of Science 2023-10-25 /pmc/articles/PMC10599553/ /pubmed/37878561 http://dx.doi.org/10.1371/journal.pdig.0000354 Text en © 2023 Naveed et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Naveed, Iqra
Kaleem, Muhammad Farhat
Keshavjee, Karim
Guergachi, Aziz
Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title_full Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title_fullStr Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title_full_unstemmed Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title_short Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
title_sort artificial intelligence with temporal features outperforms machine learning in predicting diabetes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599553/
https://www.ncbi.nlm.nih.gov/pubmed/37878561
http://dx.doi.org/10.1371/journal.pdig.0000354
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