Dataset Information

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

ABSTRACT:

Objective

We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.

Methods

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.

Results

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.

Conclusion

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.

SUBMITTER: Chowdhury MZI

PROVIDER: S-EPMC8989291 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Chowdhury Mohammad Ziaul Islam MZI Naeem Iffat I Quan Hude H Leung Alexander A AA Sikdar Khokan C KC O'Beirne Maeve M Turin Tanvir C TC

PloS one 20220407 4

<h4>Objective</h4>We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.<h4>Methods</h4>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 ...[more]

PMID: 35390039

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Dataset Information

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

Objective

Methods

Results

Conclusion

Publications

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

Similar Datasets

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