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Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population
The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440667/ https://www.ncbi.nlm.nih.gov/pubmed/30602092 http://dx.doi.org/10.5808/GI.2018.16.4.e31 |
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author | Choe, Eun Kyung Rhee, Hwanseok Lee, Seungjae Shin, Eunsoon Oh, Seung-Won Lee, Jong-Eun Choi, Seung Ho |
author_facet | Choe, Eun Kyung Rhee, Hwanseok Lee, Seungjae Shin, Eunsoon Oh, Seung-Won Lee, Jong-Eun Choi, Seung Ho |
author_sort | Choe, Eun Kyung |
collection | PubMed |
description | The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle. |
format | Online Article Text |
id | pubmed-6440667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Korea Genome Organization |
record_format | MEDLINE/PubMed |
spelling | pubmed-64406672019-04-03 Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population Choe, Eun Kyung Rhee, Hwanseok Lee, Seungjae Shin, Eunsoon Oh, Seung-Won Lee, Jong-Eun Choi, Seung Ho Genomics Inform Original Article The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle. Korea Genome Organization 2018-12 2018-12-28 /pmc/articles/PMC6440667/ /pubmed/30602092 http://dx.doi.org/10.5808/GI.2018.16.4.e31 Text en Copyright © 2018 by Korea Genome Organization It is identical to the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/). |
spellingShingle | Original Article Choe, Eun Kyung Rhee, Hwanseok Lee, Seungjae Shin, Eunsoon Oh, Seung-Won Lee, Jong-Eun Choi, Seung Ho Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title | Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title_full | Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title_fullStr | Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title_full_unstemmed | Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title_short | Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population |
title_sort | metabolic syndrome prediction using machine learning models with genetic and clinical information from a nonobese healthy population |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440667/ https://www.ncbi.nlm.nih.gov/pubmed/30602092 http://dx.doi.org/10.5808/GI.2018.16.4.e31 |
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