Uncovering key factors in weight loss effectiveness through machine learning.

Journal: International journal of obesity (2005)
Published Date: May 6, 2025

Abstract

BACKGROUND/OBJECTIVES: One of the main challenges in weight loss is the dramatic interindividual variability in response to treatment. We aim to systematically identify factors relevant to weight loss effectiveness using machine learning (ML).

Authors

  • Hui-Wen Yang
    Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA. stopstoptalking@gmail.com.
  • Rocío De la Peña-Armada
    Department of Nutrition and Food Science, Complutense University of Madrid, Madrid, Spain.
  • Haoqi Sun
    Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA.
  • Yu-Qi Peng
    Department of Biomedical Sciences and Engineering, National Central University, Taoyuan.
  • Men-Tzung Lo
    Department of Biomedical Sciences and Engineering, National Central University, Chung-li, Taiwan. Electronic address: mzlo@ncu.edu.tw.
  • Frank A J L Scheer
    Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
  • Kun Hu
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China.
  • Marta Garaulet
    Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA. garaulet@um.es.

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