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- Title
Identificación de patrones a través de algoritmos de machine learning en los casos registrados de intentos suicidas en una ciudad de Colombia.
- Authors
GUZMÁN BRAND, VÍCTOR ALFONSO; GÉLVEZ GARCÍA, LAURA ESPERANZA
- Abstract
Objective: to identify patterns in the cases of suicide attempts reported in the National Public Health Surveillance System by the mayor's office of the city of Bucaramanga, Colombia, during the period 2016-2023. Methodology: a quantitative approach is employed with the purpose of describing, explaining and predicting the phenomena studied, using the Knowledge Discovery in Databases method to guide the data mining process. Results: a high concentration of suicide attempts is shown, especially in the year 2022, underlining the importance of specific strategies for different age and gender groups. Discussion: it is highlighted that young people represent 40.5% of the cases, with an increased risk in the course of the pandemic, especially among those with mental disorders. Women account for 60.5% of cases, mostly in urban areas and in the contributory health care system. Conclusions: the importance of considering a history of previous attempts, tensions with the partner and the use of psychoactive substances as risk factors is emphasized. In addition, the connection between mental disorders and suicide attempts is highlighted, pointing out intoxication as the most common method. A higher risk is highlighted in people vulnerable to various forms of violence and precarious socioeconomic conditions. The Random Forest method proves to be effective in identifying key factors, while applied machine learning algorithms could improve early detection.
- Subjects
DATA mining; MACHINE learning; YOUNG adults; PUBLIC health surveillance; ATTEMPTED suicide; AGE groups
- Publication
Psicoespacios, 2024, Vol 18, Issue 32, p1
- ISSN
2145-2776
- Publication type
Article
- DOI
10.25057/21452776.1634