Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis

We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting t...

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Bibliographic Details
Published in:PloS one Vol. 17; no. 4; p. e0266334
Main Authors: Chowdhury, Mohammad Ziaul Islam, Naeem, Iffat, Quan, Hude, Leung, Alexander A., Sikdar, Khokan C., O’Beirne, Maeve, Turin, Tanvir C.
Format: Journal Article
Language:English
Published: United States Public Library of Science 07.04.2022
Public Library of Science (PLoS)
Subjects:
Adult
Adults
Analysis
Bias
Biology and Life Sciences
Computer and Information Sciences
Diagnosis
Estimates
Health sciences
Health surveillance
Heterogeneity
Humans
Hypertension
Hypertension - diagnosis
Learning algorithms
Machine Learning
Medicine
Medicine and Health Sciences
Meta-analysis
Modelling
Performance prediction
Physical Sciences
Population
Population statistics
Prediction models
Quality assessment
Regression Analysis
Research and Analysis Methods
Risk analysis
Risk Factors
Statistical analysis
Systematic review
Canada
Alberta Canada
Calgary Alberta Canada
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0266334