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Authors: Ariane B. da Silva 1 ; Maycoln Teodoro 2 and Cristiane 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, Adolescence, Children, Machine Learning, Instance Selection.

Abstract: Depression is the leading global cause of disability and often begins in adolescence, a critical period for developing depressive symptoms. Major depressive disorder in the early stages of life is common worldwide but challenging to diagnose. Identifying the most striking profiles of depression in children and adolescents could benefit the training and performance of Machine Learning models and thus help in the diagnosis. Instance Selection is one of the most applied methods for data reduction, allowing the most significant samples to represent them. This work seeks to improve the SI with the Ant Colony Optimization heuristic, introducing stochasticity control to better characterize profiles of children and adolescents with depression. The proposed technique increased the detection rate of individuals with high symptoms in all evaluated algorithms between 0.07 and 8.93 percentage points.

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Paper citation in several formats:
B. da Silva, A.; Teodoro, M. and Nobre, C. (2024). Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 404-411. DOI: 10.5220/0012355600003657

@conference{healthinf24,
author={Ariane {B. da Silva}. and Maycoln Teodoro. and Cristiane Nobre.},
title={Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={404-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012355600003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents
SN - 978-989-758-688-0
IS - 2184-4305
AU - B. da Silva, A.
AU - Teodoro, M.
AU - Nobre, C.
PY - 2024
SP - 404
EP - 411
DO - 10.5220/0012355600003657
PB - SciTePress

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