Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.

Journal: European journal of nutrition
Published Date: Feb 25, 2024

Abstract

PURPOSE: The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques.

Authors

  • Longfei Li
    School of Physical Education and Health, Heze University, 2269 University Road, Mudan District, Heze, 274-015, Shandong, China.
  • Haruki Momma
    Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
  • Haili Chen
    Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
  • Saida Salima Nawrin
    Division of Biomedical Engineering for Health & Welfare, Tohoku University Graduate School of Biomedical Engineering, 6-6-12, Aramaki Aza Aoba Aoba-ku, Sendai, Miyagi, 980-8579, Japan.
  • Yidan Xu
    Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
  • Hitoshi Inada
    Department of Developmental Neuroscience, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. hinada@ncnp.go.jp.
  • Ryoichi Nagatomi
    Department of Medicine and Science in Sports and Exercise, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nagatomi@med.tohoku.ac.jp.

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