Support Vector Machine to Criminal Recidivism Prediction

Abstract

Internal security of the state is one of the prerequisites for sustainable development. To ensure the public safety and personal security of citizens, it is necessary to develop effective measures to reduce crime and prevent crime in the future. The starting point for the development and practical implementation of an effective strategy to combat crime or prevent certain crimes is criminological forecasting. Individual forecasting is aimed at determining the possibility of committing a crime (crimes) in the future by a certain person or group of persons. For risk assessment, the following are traditionally used machine learning models. Such models also provide qualitative assessments in the scientific prediction of the likelihood and possibilities of committing a repeat criminal offense. Knowledge gained from the application of machine learning algorithm, can provide justice authorities with anticipatory information that is essential for developing a general concept of combating crime. The development of applied models for crime analysis and forecasting can become a reliable tool to support decision-making in predicting likely criminal behavior in the future and ensuring the internal security of the state. In this paper, the results of the application are presented by the machine-learning algorithms Support Vector Machine (SVM) for assessment of the risk of recidivism of criminal offenses by persons who have already been convicted of such a crime in the past. The data set consisted of the 12,000 criminal defendants’ criminal profile information in Ukraine. The constructed classifier has a high precision (98.67%), recall (97.53%) and is qualitative (AUC is equal 0.981). The created SVM model can be applied to new data set to predict the risk of reoffending by convicted individuals in the future