Open AccessDeath Registry Prediction in Brazilian Male Prisons with a Random Forest Ensemble
Author(s) -
Nathan Formentin,
Eduardo Euclydes de Lima e Borges,
Giancarlo Lucca,
Hélida Santos,
Graçaliz Pereira Dimuro
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12140
Subject(s) - overcrowding , random forest , prison , classifier (uml) , geography , investment (military) , population , prison population , computer science , demography , statistics , artificial intelligence , mathematics , economic growth , economics , political science , sociology , law , archaeology , politics
Brazil has the third-largest prison population globally, and it has been growing steadily for more than two decades. Constant growth and low jail investment generated significant problems, such as overcrowding and widespread diseases. This study proposes the construction of a Random Forest classifier to predict the occurrence of deaths in prisons. We extracted data from the National Survey of Penitentiary Information for the years 2015 to 2016. The best-fitted classifier achieved accuracy equals 87% being able to identify correctly up to 84% of deaths occurrences. In the present work, it was possible to establish a relationship between prisons' reality and the data mined, determining areas in need of investment in the penitentiary system.