Machine learning algorithms to predict depression in older adults in China: a cross-sectional study

Objective: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression. Methods: This research selected 7,880 older adult people by utilizing data from the 2020 China Health and Retirement Longitudinal Study. Thereafter, the dataset was classified into training and testing sets at a 6:4 ratio. Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the older adult. To compare the differences in the ROC curves of the different models, the Delong test was conducted. Meanwhile, to evaluate the models' performance, this research performed decision curve analysis (DCA). Thereafter, the Shapely Additive exPlanations values were utilized for model interpretation on the bases of the prediction results' substantial contributions. Results: The range of the area under the curve (AUC) of each model's ROC curves was 0.648-0.738, with significant differences (P < 0.01). The DCA results indicate that within various probability thresholds, LightGBM's net benefit was the highest. Self-rated health, nighttime sleep, gender, age, and cognitive function are the five most important characteristics of all models in terms of predicting the occurrence of depression. Conclusion: The occurrence of depression among China's older adult population and the critical factors leading to depression can be predicted and identified, respectively, by ML algorithms.