Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
PLoS One. 2023 Jul 13;18(7):e0288648. doi: 10.1371/journal.pone.0288648. eCollection 2023.
Patients with diabetes mellitus (DM) are twice as likely as nondiabetic individuals to develop depression, which is a prevalent but often undiagnosed psychiatric comorbidity. Patients with DM who are depressed have poor glycemic control, worse quality of life, increased risk of diabetic complications, and higher mortality rate. The present study aimed to develop machine learning (ML) models that identify depression in patients with DM, determine the best performing model by evaluating multiple ML algorithms, and investigate features related to depression. We developed six ML models, including random forest, K-nearest neighbor, support vector machine (SVM), Adaptive Boosting, light gradient-boosting machine, and Extreme Gradient Boosting, based on the Korea National Health and Nutrition Examination Survey. The results showed that the SVM model performed well, with a cross-validated area under the receiver operating characteristic curve of 0.835 (95% confidence interval [CI] = 0.730-0.901). Thirteen features were related to depression in patients with DM. Permutation feature importance showed that the most important feature was subjective health status, followed by level of general stress awareness; stress recognition rate; average monthly income; triglyceride (mg/dL) level; activity restriction status; European quality of life (EuroQoL): usual activity and lying in a sickbed in the past 1 month; EuroQoL: pain / discomfort, self-care, and physical discomfort in the last 2 weeks; and EuroQoL: mobility and chewing problems. The current findings may offer clinicians a better understanding of the relationship between DM and depression using ML approaches and may be an initial step toward developing a more predictive model for the early detection of depressive symptoms in patients with DM.
糖尿病（DM）患者患抑郁症的可能性是无糖尿病个体的两倍，而抑郁症是一种常见但常常未被诊断的精神共病。患有抑郁症的 DM 患者血糖控制较差，生活质量更差，发生糖尿病并发症的风险增加，死亡率更高。本研究旨在开发能够识别 DM 患者抑郁症的机器学习（ML）模型，通过评估多种 ML 算法来确定表现最佳的模型，并探讨与抑郁症相关的特征。我们基于韩国国家健康和营养检查调查开发了六个 ML 模型，包括随机森林、K 最近邻、支持向量机（SVM）、自适应增强、轻梯度提升机和极端梯度提升机。结果表明，SVM 模型表现良好，其交叉验证的接收器工作特征曲线下面积为 0.835（95%置信区间[CI] = 0.730-0.901）。有 13 个特征与 DM 患者的抑郁症有关。置换特征重要性显示，最重要的特征是主观健康状况，其次是一般压力意识水平；压力识别率；平均月收入；甘油三酯（mg/dL）水平；活动受限状况；欧洲生活质量（EuroQoL）：过去 1 个月内的通常活动和卧床不起；EuroQoL：过去 2 周内的疼痛/不适、自我护理和身体不适；以及 EuroQoL：移动性和咀嚼问题。目前的研究结果可能为临床医生提供使用 ML 方法更好地理解 DM 和抑郁症之间关系的认识，并可能是朝着开发更具预测性的模型以早期检测 DM 患者抑郁症状迈出的第一步。
