A Machine Learning Approach to Victimization Prediction and Prevention

Abstract

Victimization extends criminal cases to more complicated and volatile harm due to various forms of vulnerability and behavioral risk factors of victims. Multiple tools have been developed to identify victims, but few have been developed to assess, predict, and prevent potential victimization using machine learning. The objective of this research is to develop novel methods that aid in identifying potential victims to prevent crime. This paper proposed a prediction of victimization using a mixed ML/DL approach, based on a self-administered dataset of 880 individuals. The data recorded personal and behavioral characteristics. Missing data were handled, and the ML algorithms were assessed after normalization. The authors utilized several machine learning and deep learning classifiers, which were selected due to their applicability to structured survey data and their ability to model non-linear relationships flexibly. For performance evaluation, they utilized nine models. The results indicated that the K-Nearest Neighbors gained a high accuracy of 97.73% and performed well compared to other models.