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Machine learning-based predictive modeling of depression in hypertensive populations
We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the N...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337649/ https://www.ncbi.nlm.nih.gov/pubmed/35905087 http://dx.doi.org/10.1371/journal.pone.0272330 |
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author | Lee, Chiyoung Kim, Heewon |
author_facet | Lee, Chiyoung Kim, Heewon |
author_sort | Lee, Chiyoung |
collection | PubMed |
description | We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the National Health and Nutrition Examination Survey (2011–2020). We selected several significant features using feature selection methods to build the models. Data imbalance was managed with random down-sampling. Six different ML classification methods implemented in the R package caret—artificial neural network, random forest, AdaBoost, stochastic gradient boosting, XGBoost, and support vector machine—were employed with 10-fold cross-validation for predictions. Model performance was assessed by examining the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1-score. For an interpretable algorithm, we used the variable importance evaluation function in caret. Of all classification models, artificial neural network trained with selected features (n = 30) achieved the highest AUC (0.813) and specificity (0.780) in predicting depression. Support vector machine predicted depression with the highest accuracy (0.771), precision (0.969), sensitivity (0.774), and F1-score (0.860). The most frequent and important features contributing to the models included the ratio of family income to poverty, triglyceride level, white blood cell count, age, sleep disorder status, the presence of arthritis, hemoglobin level, marital status, and education level. In conclusion, ML algorithms performed comparably in predicting depression among hypertensive populations. Furthermore, the developed models shed light on variables’ relative importance, paving the way for further clinical research. |
format | Online Article Text |
id | pubmed-9337649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93376492022-07-30 Machine learning-based predictive modeling of depression in hypertensive populations Lee, Chiyoung Kim, Heewon PLoS One Research Article We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the National Health and Nutrition Examination Survey (2011–2020). We selected several significant features using feature selection methods to build the models. Data imbalance was managed with random down-sampling. Six different ML classification methods implemented in the R package caret—artificial neural network, random forest, AdaBoost, stochastic gradient boosting, XGBoost, and support vector machine—were employed with 10-fold cross-validation for predictions. Model performance was assessed by examining the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1-score. For an interpretable algorithm, we used the variable importance evaluation function in caret. Of all classification models, artificial neural network trained with selected features (n = 30) achieved the highest AUC (0.813) and specificity (0.780) in predicting depression. Support vector machine predicted depression with the highest accuracy (0.771), precision (0.969), sensitivity (0.774), and F1-score (0.860). The most frequent and important features contributing to the models included the ratio of family income to poverty, triglyceride level, white blood cell count, age, sleep disorder status, the presence of arthritis, hemoglobin level, marital status, and education level. In conclusion, ML algorithms performed comparably in predicting depression among hypertensive populations. Furthermore, the developed models shed light on variables’ relative importance, paving the way for further clinical research. Public Library of Science 2022-07-29 /pmc/articles/PMC9337649/ /pubmed/35905087 http://dx.doi.org/10.1371/journal.pone.0272330 Text en © 2022 Lee, Kim 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 Lee, Chiyoung Kim, Heewon Machine learning-based predictive modeling of depression in hypertensive populations |
title | Machine learning-based predictive modeling of depression in hypertensive populations |
title_full | Machine learning-based predictive modeling of depression in hypertensive populations |
title_fullStr | Machine learning-based predictive modeling of depression in hypertensive populations |
title_full_unstemmed | Machine learning-based predictive modeling of depression in hypertensive populations |
title_short | Machine learning-based predictive modeling of depression in hypertensive populations |
title_sort | machine learning-based predictive modeling of depression in hypertensive populations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337649/ https://www.ncbi.nlm.nih.gov/pubmed/35905087 http://dx.doi.org/10.1371/journal.pone.0272330 |
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