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A machine learning approach to optimize the assessment of depressive symptomatology
Date
2022
Abstract
In this work, we developed a machine learning model to predict depressive symptomatology (DS). The model achieved an AUC of 0.71 and a sensitivity of 74.8%. When developing the model, we applied a Bayesian network approach to select its predictors. This probabilistic approach facilitates the understanding of the relationships between predictors and DS. In consequence, we were able to identify that having balance problems and experiencing shortness of breath are directly related to DS.
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Publisher
Elsevier
Citation
Procedia Computer Science, 2022, 206, pp. 111-120
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Funding Information
This work has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 18/CRT/6049 (E.M). This study was funded by São Paulo Research Foundation (FAPESP, process number 2017/50094‐2) and the Joint Global Health Trials initiative jointly funded by Medical Research Council, Wellcome Trust, and the UK Department for International Development (MRC, process number MR/R006229/1). CAN is supported by FAPESP (2018/19343-9) and MS by CNPq-Brazil (307579/2-19-0).
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Article
Rights
https://creativecommons.org/licenses/by-nc-sa/4.0/