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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...

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Autores principales: Lee, Chiyoung, Kim, Heewon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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