Predicting youth diabetes risk using NHANES data and machine learning

Abstract Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a larg...

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Autores principales:	Nita Vangeepuram, Bian Liu, Po-hsiang Chiu, Linhua Wang, Gaurav Pandey
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8e6be70c890b40e583fd13c28f1b28c2
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spelling	oai:doaj.org-article:8e6be70c890b40e583fd13c28f1b28c22021-12-02T14:47:38ZPredicting youth diabetes risk using NHANES data and machine learning10.1038/s41598-021-90406-02045-2322https://doaj.org/article/8e6be70c890b40e583fd13c28f1b28c22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90406-0https://doaj.org/toc/2045-2322Abstract Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.Nita VangeepuramBian LiuPo-hsiang ChiuLinhua WangGaurav PandeyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nita Vangeepuram Bian Liu Po-hsiang Chiu Linhua Wang Gaurav Pandey Predicting youth diabetes risk using NHANES data and machine learning
description	Abstract Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.
format	article
author	Nita Vangeepuram Bian Liu Po-hsiang Chiu Linhua Wang Gaurav Pandey
author_facet	Nita Vangeepuram Bian Liu Po-hsiang Chiu Linhua Wang Gaurav Pandey
author_sort	Nita Vangeepuram
title	Predicting youth diabetes risk using NHANES data and machine learning
title_short	Predicting youth diabetes risk using NHANES data and machine learning
title_full	Predicting youth diabetes risk using NHANES data and machine learning
title_fullStr	Predicting youth diabetes risk using NHANES data and machine learning
title_full_unstemmed	Predicting youth diabetes risk using NHANES data and machine learning
title_sort	predicting youth diabetes risk using nhanes data and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8e6be70c890b40e583fd13c28f1b28c2
work_keys_str_mv	AT nitavangeepuram predictingyouthdiabetesriskusingnhanesdataandmachinelearning AT bianliu predictingyouthdiabetesriskusingnhanesdataandmachinelearning AT pohsiangchiu predictingyouthdiabetesriskusingnhanesdataandmachinelearning AT linhuawang predictingyouthdiabetesriskusingnhanesdataandmachinelearning AT gauravpandey predictingyouthdiabetesriskusingnhanesdataandmachinelearning
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Predicting youth diabetes risk using NHANES data and machine learning

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