AUTHOR=Fujihara Kazuya , Yamada Harada Mayuko , Horikawa Chika , Iwanaga Midori , Tanaka Hirofumi , Nomura Hitoshi , Sui Yasuharu , Tanabe Kyouhei , Yamada Takaho , Kodama Satoru , Kato Kiminori , Sone Hirohito 

TITLE=Machine learning approach to predict body weight in adults

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1090146

DOI=10.3389/fpubh.2023.1090146

ISSN=2296-2565

ABSTRACT=Background: Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings. 
Methods: We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19 to 91 y who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent three years were validated for 5000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression.
Results: The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 y) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (P =0.323).
Conclusions: The HMLT-based machine learning model could successfully predict weight change over three years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.