Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning

A plethora of studies have unequivocally established the profound significance of harmonious familial relationships on the psychological well-being of the elderly. In this study, we elucidate the intergenerational relationships, probing the association between frequent interactions or encounters with their children and the incidence of depression in old age. We employed a retrospective cross-sectional study design, sourcing our data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS). To identify cases of depression, we utilized the 10-item Center for Epidemiologic Studies Depression Scale (CESD). Employing a five-fold cross-validation methodology, we endeavored to fashion five distinct machine learning models. Subsequently, we crafted learning curves to facilitate the refinement of hyperparameters, assessing model classification performance through metrics such as accuracy and the Area Under the Receiver Operating Characteristic (AUROC) curve. To further elucidate the relationship between variables and geriatric depression, logistic regression was subsequently applied. Our findings accentuated that sleep patterns emerged as the paramount determinants influencing the onset of depression in the elderly. Relationships with offspring ranked as the second most significant determinant, only surpassed by sleep habits. A negative correlation was observed between sleep patterns (Odds Ratio [OR]: 0.78, 95 % Confidence Interval [CI]: 0.75–0.81, P