Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan.
Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan.
Front Public Health. 2023 Jun 15;11:1090146. doi: 10.3389/fpubh.2023.1090146. eCollection 2023.
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.
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 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression.
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/m) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 ( = 0.323).
The HMLT-based machine learning model could successfully predict weight change over 3 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.
肥胖是 2 型糖尿病、高血压和心血管疾病等非传染性疾病的既定危险因素。因此，控制体重是非传染性疾病预防的关键因素。一种简单快速的方法来预测未来几年的体重变化可能有助于临床体重管理。
我们利用机器学习模型，使用大数据来预测未来 3 年内体重的变化。机器学习模型的输入是 50000 名年龄在 19-91 岁的日本人（32977 名男性）的三年数据，他们接受了年度健康检查。使用异构混合学习技术（HMLT）预测随后 3 年内体重的预测公式在 5000 人身上进行了验证。均方根误差（RMSE）用于评估与多元回归相比的准确性。
利用 HMLT 的机器学习模型自动生成了五个预测公式。研究发现，在基线时体重指数（BMI）较高（BMI≥29.93kg/m）和 BMI 较低（<23.44kg/m）的年轻人（<24 岁）中，生活方式对体重的影响较大。验证集中的 RMSE 为 1.914，反映了与多元回归模型（1.890）（=0.323）相当的能力。
基于 HMLT 的机器学习模型可以成功预测 3 年内的体重变化。我们的模型可以自动识别对体重减轻影响较大的生活方式组和影响个体体重变化的因素。虽然在广泛应用于全球临床环境之前，该模型必须在其他人群（包括其他种族群体）中进行验证，但结果表明，该机器学习模型可以为个体化体重管理做出贡献。
