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Machine learning-based predictive model for prevention of metabolic syndrome
Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237504/ https://www.ncbi.nlm.nih.gov/pubmed/37267302 http://dx.doi.org/10.1371/journal.pone.0286635 |
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author | Shin, Hyunseok Shim, Simon Oh, Sejong |
author_facet | Shin, Hyunseok Shim, Simon Oh, Sejong |
author_sort | Shin, Hyunseok |
collection | PubMed |
description | Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model’s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome. |
format | Online Article Text |
id | pubmed-10237504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102375042023-06-03 Machine learning-based predictive model for prevention of metabolic syndrome Shin, Hyunseok Shim, Simon Oh, Sejong PLoS One Research Article Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model’s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome. Public Library of Science 2023-06-02 /pmc/articles/PMC10237504/ /pubmed/37267302 http://dx.doi.org/10.1371/journal.pone.0286635 Text en © 2023 Shin 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 Shin, Hyunseok Shim, Simon Oh, Sejong Machine learning-based predictive model for prevention of metabolic syndrome |
title | Machine learning-based predictive model for prevention of metabolic syndrome |
title_full | Machine learning-based predictive model for prevention of metabolic syndrome |
title_fullStr | Machine learning-based predictive model for prevention of metabolic syndrome |
title_full_unstemmed | Machine learning-based predictive model for prevention of metabolic syndrome |
title_short | Machine learning-based predictive model for prevention of metabolic syndrome |
title_sort | machine learning-based predictive model for prevention of metabolic syndrome |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237504/ https://www.ncbi.nlm.nih.gov/pubmed/37267302 http://dx.doi.org/10.1371/journal.pone.0286635 |
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