Authors:
Caibe Pereira
1
;
Rômulo Peixoto
2
;
Manuella Kaster
1
;
Mateus Grellert
3
and
Jônata Carvalho
2
Affiliations:
1
Biochemistry Department, Federal University of Santa Catarina, Florianopolis, Brazil
;
2
Informatics and Statistics Department, Federal University of Santa Catarina, Florianopolis, Brazil
;
3
Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Keyword(s):
SUS, Health Infrastructure, Machine Learning.
Abstract:
Suicide is a multifactorial, complex condition and one of the leading global causes of death, with suicide attempt as the main risk factor. To this day, studies have shown relevant indicators that help identify people with risk of committing suicide, but the literature still lacks comprehensive studies that evaluate how different risk factors interact and ultimately affects the suicide risk. In this paper, we aimed to identify patterns in data from the Brazilian Unified Health System – SUS, from 2009 to 2020, of individual reports of suicide attempts and suicide deaths in the Brazilian Southern States, integrating those with a database of the healthcare infrastructure. We framed the problem as a classification task for each micro-region to predict suicide and reattempt rate as low, moderate, or high. We developed a pipeline for integrating, cleaning, and selecting the data, and trained and compared three machine learning models: Decision Tree, Random Forest, and XGBoost, with approxi
mately 97% accuracy. The most important features for predicting suicide rates were the number of mental health units and clinics, and for both suicide and reattempts were the number of physicians and nurses available. This novel result brings valuable knowledge on possible directions for governmental investments in order to reduce suicide rates.
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