Authors:
Eduardo Paraíso
;
Ariane B. da Silva
and
Cristiane Nobre
Affiliation:
Institute of Exact Sciences and Informatics, Pontifical Catholic University of Minas Gerais, Dom José Gaspar, Belo Horizonte, Brazil
Keyword(s):
Older Adults, Depression, Underdiagnosis, Machine Learning, Risk Factors.
Abstract:
According to the World Health Organization, the total number of people living with depression worldwide is more than 300 million, with depressive disorders ranked globally as the third leading cause of disability. Among older adults, depression is the most common mental illness. This study addressed the cultural stigma surrounding depression in older adults and investigated factors contributing to underdiagnosis and undertreatment. We used data from older adults participating in the National Health Survey (NHS). We applied machine learning algorithms to predict the disorder (Random Forest, Support Vector Machine, Logistic Regression, Gradient Boost, XGBoost, Decision Tree, and Multilayer Neural Network), carefully interpreting the result obtained. Through the interpretability of ML models, the study identified risk factors associated with depression, and using silhouette index and attribute comparison, we found evidence of potential individuals who, although undiagnosed, may be suffe
ring or about to suffer from depression, requiring appropriate care and treatment. This study represents a significant advance in mitigating the impact of cultural stigma on mental health diagnoses in the older population in Brazil.
(More)