Predicting Depression in Children and Adolescents using the SHAP Approach

Marcelo Balbino, Marcelo Balbino, Renata Santana, Maycoln Teodoro, Mark Song, Luis Zárate, Cristiane Nobre

2022

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, excessive 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.

Download


Paper Citation


in Harvard Style

Balbino M., Santana R., Teodoro M., Song M., Zárate L. and Nobre C. (2022). Predicting Depression in Children and Adolescents using the SHAP Approach. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF, ISBN 978-989-758-552-4, pages 514-521. DOI: 10.5220/0010842500003123


in Bibtex Style

@conference{healthinf22,
author={Marcelo Balbino and Renata Santana and Maycoln Teodoro and Mark Song and Luis Zárate and Cristiane Nobre},
title={Predicting Depression in Children and Adolescents using the SHAP Approach},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF,},
year={2022},
pages={514-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010842500003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF,
TI - Predicting Depression in Children and Adolescents using the SHAP Approach
SN - 978-989-758-552-4
AU - Balbino M.
AU - Santana R.
AU - Teodoro M.
AU - Song M.
AU - Zárate L.
AU - Nobre C.
PY - 2022
SP - 514
EP - 521
DO - 10.5220/0010842500003123