Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach

Stephanie Ruth Basilio Silva Gomez, Malcolm von Schantz, Mario A. Leocadio-Miguel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Comorbid depression is a highly prevalent and debilitating condition in middle-aged and elderly adults, particularly when associated with obesity, diabetes, and sleep disturbances. In this context, there is a growing need to develop efficient screening methods for cases based on clinical health markers for these comorbidities and sleep data. Thus, our objective was to detect depressive symptoms in these subjects, considering general biomarkers of obesity and diabetes and variables related to sleep and physical exercise through a machine learning approach. Methods: National Health and Nutrition Examination Survey (NHANES) 2015-2016 data were used and eighteen variables on self-reported physical activity, self-reported sleep habits, sleep disturbance indicative, anthropometric measurements, sociodemographic characteristics and plasma biomarkers of obesity and diabetes were selected as predictors. A total of 2,907 middle-aged and elderly subjects were eligible for the
study. Supervised learning algorithms such as Lasso penalized Logistic Regression (LR), Random forest (RF) and Extreme Gradient Boosting (XGBoost) were implemented. Results: XGBoost provided greater accuracy and precision (87%), with a proportion of hits in cases with depressive symptoms above 80%. In addition, daytime sleepiness was the most significant predictor variable for predicting depressive symptoms. Conclusions: Sleep and physical activity variables, in addition to obesity and diabetes biomarkers, together assume significant importance to predict, with accuracy and precision of 87%, the occurrence of depressive symptoms in middle- aged and elderly individuals.
Original languageEnglish
JournalSleep Medicine
Early online date3 Jan 2023
DOIs
Publication statusE-pub ahead of print - 3 Jan 2023

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