Homicide Prediction Model in Bogotá Using the Decision Tree Regression Algorithm

Authors

  • Simanca H. Fredys A. Universidad Cooperativa de Colombia
  • Abuchar Porras Alexandra Universidad Distrital Francisco José de Caldas
  • Anzola John Fundación Universitaria Los Libertadores
  • Palacios Jairo Jamith Docente de Planta, Colegio Mayor de Cundinamarca
  • Suarez Roldán Carolina Universidad Cooperativa de Colombia
  • Lugo Manuel Barbosa Guerrero Docente de Planta, Colegio Mayor de Cundinamarca

Keywords:

Machine Learning, Homicides, Bogota, Decision Tree

Abstract

Bogota is the capital of Colombia, and like many capitals in the world it faces challenges related to security and specifically homicides. Throughout the city's existence, the homicide rate has varied due to multiple political, social, and economic factors. These indicators have always been high and quite significant and a constant concern for the city authorities. However, the use of Machine Learning algorithms to predict homicides is a controversial application, but one of growing interest for authorities and experts in Data Mining. For this reason, the development of a Regression algorithm is proposed, specifically the Decision Tree algorithm that predicts the number of homicides in the city of Bogotá, applicable to any city in Colombia, seeking to identify the potential that this tool may have in the planning of prevention strategies. The design and validation of the algorithm yielded an accuracy between 70% and 75%, which is not a desired percentage, but neither can be ruled out in the framework of the use of prediction algorithms. Finally, it is important to point out that this issue should be approached with caution and responsibility and not fall into the promotion of profiles based on stereotypes or the reinforcement of negative stereotypes.

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Published

24.11.2023

How to Cite

Fredys A., S. H. ., Alexandra, A. P. ., John, A. ., Jamith, P. J. ., Carolina, S. R. ., & Guerrero, L. M. B. . (2023). Homicide Prediction Model in Bogotá Using the Decision Tree Regression Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 521–529. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3961

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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