Diagrammatic representation of the overall workflow of our experiment to select the optimum model.

<p>The red arrow illustrates that model training may loop back to estimator selection [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0322299#pone.0322299.ref058" target="_blank">58</a>], pre-processing, feature engineering, and parameter...

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Bibliographic Details
Main Author:	Giuliano Lorenzoni (21440268) (author)
Other Authors:	Cristina Tavares (15367321) (author), Nathalia Nascimento (21440271) (author), Paulo Alencar (4382428) (author), Donald Cowan (3451277) (author)
Published:	2025
Subjects:	Immunology Cancer Science Policy Mental Health Biological Sciences not elsewhere classified traumatic stress disorder still several challenges public health agencies improved experimental settings natural language processing ml classification algorithms experimental case study common mental disorders background .</ b depression detection approaches mental disorders case study depression detection xgboost models svm model specialized personnel significantly higher significantly benefited reduce costs recent research random forest people worldwide parameter setting paper tackles model choices major comorbidities machine learning highly dependent feature selection expert professionals data cleaning comprehensive assessments comparable literature build models become one affected millions
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Diagrammatic representation of the overall workflow of our experiment to select the optimum model.

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