Gutiérrez-Gallego Alberto, Zamorano-León José Javier, Parra-Rodríguez Daniel, Zekri-Nechar Khaoula, Velasco José Manuel, Garnica Óscar, Jiménez-García Rodrigo, López-de-Andrés Ana, Cuadrado-Corrales Natividad, Carabantes-Alarcón David, Lahera Vicente, Martínez-Martínez Carlos Hugo, Hidalgo J Ignacio
Department of Computer Architecture, School of Informatic, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Public Health and Maternal-Child Health Department, School of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain.
J Pers Med. 2024 Jul 31;14(8):816. doi: 10.3390/jpm14080816.
(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.
(1) 背景：运用机器学习技术的人工智能或许能帮助我们预测和预防肥胖。目的是基于不同机器学习技术的组合设计一种可解释的超重/肥胖风险预测算法。(2) 方法：收集了马德里1179名居民与社会人口统计学、生活方式及健康方面相关的38个变量，并用于训练预测模型。对九种经典机器学习技术以及基于这些经典机器学习技术组合的预测模型进行了准确性、精确性和召回率指标的测试与比较。进行了统计验证。使用SHAP（SHapley Additive exPlanations）技术来识别对体重增加影响最大的变量。(3) 结果：与所有测试的机器学习技术相比，结合梯度提升、随机森林和逻辑回归模型的级联分类器模型对超重/肥胖显示出最佳预测结果，对体重增加预测的准确率达到79%，精确率达到84%，召回率达到89%。年龄、性别、学术水平、职业、吸烟习惯、饮酒量和地中海饮食依从性对肥胖预测的影响最大。(4) 结论：与单独使用机器学习技术相比，机器学习技术的组合在预测超重/肥胖风险的准确性上有显著提高。
