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Universidad y Salud

versão impressa ISSN 0124-7107
versão On-line ISSN 2389-7066

Resumo

TIMARAN-PEREIRA, Ricardo; CALDERON-ROMERO, Andrés  e  HIDALGO-TROYA, Arsenio. Application of decision trees in the identification of patterns of fatal injuries by external cause in the municipality of Pasto, Colombia. Univ. Salud [online]. 2017, vol.19, n.3, pp.388-399. ISSN 0124-7107.  http://dx.doi.org/10.22267/rus.171903.101.

Introduction:

The Pan American Health Organization (PHO) and the World Health Organization (WHO) accepted, since the year 1993 and 1996 respectively, that violence is a public health problem, a situation that is corroborated in the report on violence and health, in which Latin America presented a homicide rate of 18 per 100,000 people, and it is considered one of the most violent regions in the world.

Objective:

To detect criminal patterns with data mining techniques in the Crime Observatory of the municipality of Pasto (Colombia).

Materials and methods:

Cross Industry Standard Process for Data Mining (CRISP-DM) was applied, which is one of the methodologies used in the development of data mining projects in academic and industrial environments. The source of information was the Crime Observatory of the municipality of Pasto, where the historical clean and transformed figures on the injuries of external cause (fatal and nonfatal) recorded in 11 years are stored.

Results:

A decision tree-based classification model was built that allowed the discovery of patterns of deaths from external causes. In the case of homicide, these happened mostly in the commune 5 in Pasto under the following circumstances: during the weekends, in the early morning, in the second semester of the year and in the public thoroughfare; besides, the victims were adult men of various professions; and the cause of the homicides were quarrels and they were produced with a fire gun.

Conclusion:

The generated knowledge will help government and security agencies make effective decisions regarding the implementation of crime prevention and citizen security plans.

Palavras-chave : Pattern recognition, automated; data mining; decision trees; classification.

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