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

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  • Mohammad Ziaul Islam Chowdhury
  • Iffat Naeem
  • Hude Quan
  • Alexander A Leung
  • Khokan C Sikdar
  • Maeve O’Beirne
  • Tanvir C Turin

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.

Suggested Citation

  • Mohammad Ziaul Islam Chowdhury & Iffat Naeem & Hude Quan & Alexander A Leung & Khokan C Sikdar & Maeve O’Beirne & Tanvir C Turin, 2022. "Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-30, April.
  • Handle: RePEc:plo:pone00:0266334
    DOI: 10.1371/journal.pone.0266334
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    References listed on IDEAS

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    1. Bum Ju Lee & Jong Yeol Kim, 2014. "A Comparison of the Predictive Power of Anthropometric Indices for Hypertension and Hypotension Risk," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    2. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
    3. Dongdong Sun & Jielin Liu & Lei Xiao & Ya Liu & Zuoguang Wang & Chuang Li & Yongxin Jin & Qiong Zhao & Shaojun Wen, 2017. "Recent development of risk-prediction models for incident hypertension: An updated systematic review," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-19, October.
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    1. Yi-Hsueh Liu & Szu-Chia Chen & Wen-Hsien Lee & Ying-Chih Chen & Po-Chao Hsu & Wei-Chung Tsai & Chee-Siong Lee & Tsung-Hsien Lin & Chih-Hsing Hung & Chao-Hung Kuo & Ho-Ming Su, 2022. "Prognostic Factors of New-Onset Hypertension in New and Traditional Hypertension Definition in a Large Taiwanese Population Follow-up Study," IJERPH, MDPI, vol. 19(24), pages 1-10, December.

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