Risk prediction models of depression in older adults with chronic diseases

Detecting potential depression and identifying the critical predictors of depression among older adults with chronic diseases are essential for timely intervention and management of depression. Therefore, risk predictive models of depression in elderly people should to be further explored. A total of 3959 respondents aged 60 years or older from the wave four survey of the China Health and Retired Longitudinal Study (CHARLS) were included in this study. We used five machine learning (ML) algorithms and three data balancing techniques to construct risk prediction models (RPMs) of depression and calculated feature importance scores to determine which features are essential to depression. The prevalence of depression was 19.2 % among older Chinese adults with chronic diseases in the wave four survey. The random forest (RF) model was more accurate than the other models after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE), with an area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of 0.957 and 0.920, respectively, a balanced accuracy of 0.891 and a sensitivity of 0.875. Furthermore, we further identified several important predictors among different sex patients. Further research on the clinical impact study of our models and external validation are needed. After several techniques were used to address class imbalanced problem, most RPMs achieved satisfactory accuracy in predicting depression among elderly people with chronic diseases. The RPMs may thus become valuable screening tools for both older individuals and healthcare practitioners to assess the risk of depression.