Authors:
Ariane C. B. da Silva
1
;
Renata C. Santana
1
;
Thiago H. N. de Lima
1
;
Maycoln L. M. Teodoro
2
;
Mark A. Song
1
;
Luis E. Zárate
1
and
Cristiane N. Nobre
1
Affiliations:
1
Institute of Exact Sciences and Informatics, Pontifical Catholic University of Minas Gerais, Dom José Gaspar, Belo Horizonte, Brazil
;
2
Department of Psychology, Federal University of Minas Gerais, Belo Horizonte, Brazil
Keyword(s):
Depression, Data Analysis, Machine Learning, Instruments.
Abstract:
Depression is a mental health disorder that affects millions of people worldwide. The disorder results from a complex interaction of biological, psychological, and social factors, leading to difficulty in both prognosis and diagnosis. In this work, we performed a review on studies about depression, to identify the main computational techniques used to support the prediction (prognosis and diagnosis) of depression, and the main attributes that might influence the development of the disorder. Our results indicate that, in the last ten years, Logistic Regression, Machine Learning techniques such as Support Vector Machines and Neural Networks, and other supervised learning algorithms, have been employed more frequently for studies predicting depression and selecting features related to it. Attributes like insomnia, gender, marital state, and use of tobacco, for example, were related to the development of depression. The review indicated growing effectiveness in using machine learning met
hods for analyzing data related to depression.
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