Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study

Abstract

BackgroundDepression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches.MethodsA prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC).ResultsThe mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest.ConclusionsThe three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.

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“…Thus, exploring the time-dependent relationship between these risk factors can help medical workers better predict the risk of depression. Moreover, studies estimating depression for population of home-based older adults are scarce, but home-based eldercare will remain the main selection for older Chinese in a long-term period of future, due to the ethics of Chinese "filial piety" [16].…”

Editorial: Late-onset depression and mania: Diagnosis, treatment and life events as risk factors

“…They identified three clinical profiles, including minimal depression, mild depression, and moderate-severe depression. ( 7 ) applied machine learning approaches to identify risk factors for depression in home-based elderly Chinese aged ≥45. The study capitalized on a large sample size and a longitudinal design, using data from a nationally representative cohort study.…”