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A Comparison of the Predictive Power of Anthropometric Indices for Hypertension and Hypotension Risk

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  • Bum Ju Lee
  • Jong Yeol Kim

Abstract

Background and Aims: It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men. Methods: For 12789 subjects 21–85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques. Results: The best indicator for predicting hypertension was rib circumference in both women (p =

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0084897
    DOI: 10.1371/journal.pone.0084897
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    Cited by:

    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.
    2. Bum Ju Lee & Jong Yeol Kim, 2016. "Identification of Hemoglobin Levels Based on Anthropometric Indices in Elderly Koreans," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-12, November.
    3. 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.

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