ArticlePloS one2022
Machine learning-based predictive modeling of depression in hypertensive populations.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 60 citations in OpenAlex.
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- Depression prediction and key factors: A comparative analysis of logistic regression and machine learning models.PloS one · 2026Article
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- Circadian Rhythm Genes-based Prognostic Signature for Bladder Cancer: Association of EZH2 Expression with Anesthetic-related Changes in Circulating Tumor Cells.Current medicinal chemistry · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Identification and validation of biomarkers related to mitochondria-associated endoplasmic reticulum membranes in type 2 diabetes mellitus using peripheral blood transcriptomics.European journal of medical research · 2025Article
- Risk model for depression in patients with chronic prostatitis/chronic pelvic pain syndrome: A retrospective cross-sectional study.The Journal of international medical research · 2025Article
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- Association of plasma homocysteine with cardiovascular disease in American adults: a study based on the national health and nutrition examination survey database.Frontiers in cardiovascular medicine · 2025Article
- Article
- Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.International journal of molecular sciences · 2024Article
- Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.BMC psychology · 2024Article
- A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration.BMC genomics · 2024Article
- Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative ColitisEndocrine, metabolic & immune disorders drug targets · 2024Article
- Application of machine learning algorithms to identify people with low bone density.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.