Authors:
Marcelo Balbino
1
;
2
;
Renata Santana
2
;
Maycoln Teodoro
3
;
Mark Song
2
;
Luis Zárate
2
and
Cristiane Nobre
2
Affiliations:
1
Department of Computing and Civil Construction, Federal Center for Technological Education of Minas Gerais, Brazil
;
2
Department of Computing, Pontifical Catholic University of Minas Gerais University, Brazil
;
3
Department of Psychology, Federal University of Minas Gerais, Brazil
Keyword(s):
Depression, Machine Learning, Interpretability, SHAP.
Abstract:
Depression is a disease with severe consequences that affects millions of people, with the onset of the first symptoms being common in youth. It is essential to identify and treat individuals with depression as early as possible to prevent the losses caused by the disorder throughout life. However, the diagnostic criteria of depressive disorders for children/adolescents or adults is not differentiated, even though authors claim that the particularities of childhood must be considered. This may be why childhood depression is being underdiagnosed. Therefore, this work aims to discover the most significant features in diagnosing depression in children and adolescents through Machine Learning methods and the SHAP approach. Models with Machine Learning algorithms were developed, and the model with SVM presented the best results. The application of SHAP proved to be fundamental to deepen the understanding of this model. The experiments indicated that feelings of isolation, sadness, excessi
ve worry, complaints about one’s appearance, resistance to academic tasks, and the mother’s schooling are the most significant features in predicting depression in children and adolescents. Such results can help to understand depression in these individuals and thus lead to appropriate treatment.
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