Análise preditiva de variáveis de sono e biomarcadores de comorbidades na detecção de sintomas depressivos em adultos de meia-idade e idosos: uma abordagem de aprendizagem de máquinas
Introduction: studies have shown high incidences of depression worldwide and its cooccurrence with several important medical conditions, especially in middle-aged and elderly subjects. In this multimorbidity scenario, depression is commonly associated with diseases related to metabolic syndrome, s...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/49496 |
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| Resumo: | Introduction: studies have shown high incidences of depression worldwide and its cooccurrence with several important medical conditions, especially in middle-aged and elderly
subjects. In this multimorbidity scenario, depression is commonly associated with diseases
related to metabolic syndrome, such as obesity and diabetes. Chronic alterations in circadian
sleep-wake rhythm represent a relationship with the development of depression and its
associated comorbidities, as they favor the breakdown of the internal temporal organization
of essential physiological and metabolic processes. Currently, making accurate clinical
diagnoses and screenings have been a persistent challenge in mental health, due to the use of
limited traditional tools that do not include additional characteristics of important clinical
data of the patient, including objective observations of disease biomarkers. Objective: Thus,
the objective of the present study was to detect depressive symptomatology from general
biomarkers of obesity and diabetes, as well as variables related to sleep and physical activity,
in middle-aged and elderly adults, through a learning approach of machines. Method: Data
from the Global Physical Activity Questionnaire (GPAQ - physical activity level), from the
Patient Health Questionnaire (PHQ-9), and from the sleep habits questionnaire were extracted
from the National Health and Nutrition Examination Survey database (NHANES) in the
period 2015-2016. Other variables were accessed and used as predictive resources, such as
anthropometric measurements and plasmatic biomarkers of obesity and diabetes. A total of
2907 middle-aged and elderly adult subjects were eligible for the study. Three supervised
learning algorithms were implemented: Penalized Logistic Regression with Lasso (RL),
Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Results: The XGBoost
model provided greater accuracy and precision (87%), with a proportion of correct answers in
cases with depressive symptoms above 80%. In addition, daytime sleepiness was the
predictor variable that best contributed to predicting depressive symptoms. Conclusions:
Sleep and physical activity variables, in addition to obesity and diabetes biomarkers, together
assume significant importance in predicting, with an accuracy and precision of 87%, the
occurrence of depressive symptoms in middle-aged and elderly individuals. |
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