Chen Zhao, Liu Hao, Zhang Yao, Xing Fei, Jiang Jiabao, Xiang Zhou, Duan Xin
Department of Orthopedic Surgery, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China.
Department of Emergency Management, Sichuan Center for Diseases Control and Prevention, Chengdu, China.
Front Public Health. 2025 Feb 21;13:1472050. doi: 10.3389/fpubh.2025.1472050. eCollection 2025.
It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed for adults who live alone.
This study aims to investigate the effectiveness of utilizing personal health data in combination with a stacked ensemble machine learning (SEML) technique to detect MDD among adults living alone, seeking to gain insights into the interaction between personal health data and MDD.
Our data originated from the US National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018. We finally selected a set of 30 easily accessible variables encompassing demographic profiles, lifestyle factors, and baseline health conditions. We constructed a SEML model for MDD detection, incorporating three conventional machine learning algorithms as base models and a Neural Network (NN) as the meta-model. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to explain the impact of each predictor on MDD.
The study included 2,642 adult participants who lived alone, of whom 10.6% (279 out of 2,642) had a PHQ-9 score of 10 or above, indicating the presence of MDD. The performance of our SEML model was robust, with an area under the curve (AUC) of 0.85. Further analysis using SHAP revealed positive correlations between the occurrence of MDD and factors such as sleep disorders, number of prescription medications, need for specific walking aids, leak urine during nonphysical activities, chronic bronchitis, and Healthy Eating Index (HEI) scores for sodium. Conversely, age, the Family Monthly Poverty Level Index (FMMPI), and HEI scores for added sugar showed negative correlations with MDD occurrence. Additionally, a U-shaped relationship was noted between the occurrence of MDD and both sleep duration and Body Mass Index (BMI), as well as HEI scores for dairy.
The study has successfully developed a predictive model for MDD, specifically tailored for adults living alone using a stacked ensemble technique, enhancing the identification of MDD and its risk factors among adults living alone.
人们越来越认识到，独居成年人患重度抑郁症（MDD）的可能性高于与他人同住的成年人。然而，目前仍没有专门为独居成年人设计的MDD预测模型。
本研究旨在探讨结合个人健康数据与堆叠集成机器学习（SEML）技术在独居成年人中检测MDD的有效性，以深入了解个人健康数据与MDD之间的相互作用。
我们的数据来自2007年至2018年的美国国家健康与营养检查调查（NHANES）。我们最终选择了一组30个易于获取的变量，包括人口统计学特征、生活方式因素和基线健康状况。我们构建了一个用于MDD检测的SEML模型，将三种传统机器学习算法作为基础模型，并将神经网络（NN）作为元模型。此外，使用SHapley加性解释（SHAP）分析来解释每个预测因子对MDD的影响。
该研究纳入了2642名独居成年参与者，其中10.6%（2642人中的279人）的PHQ-9得分在10分及以上，表明存在MDD。我们的SEML模型性能稳健，曲线下面积（AUC）为0.85。使用SHAP进行的进一步分析显示，MDD的发生与睡眠障碍、处方药数量、特定助行器需求、非体力活动时漏尿、慢性支气管炎以及钠的健康饮食指数（HEI）得分等因素呈正相关。相反，年龄、家庭月贫困水平指数（FMMPI）和添加糖的HEI得分与MDD的发生呈负相关。此外，还发现MDD的发生与睡眠时间、体重指数（BMI）以及乳制品的HEI得分之间存在U型关系。
该研究成功开发了一种针对MDD的预测模型，专门使用堆叠集成技术为独居成年人量身定制，增强了对独居成年人中MDD及其风险因素的识别。
