Violence Detection with Machine Learning: A Sociodemographic Approach

Authors : Tolga ENSARİ, Betul ENSARİ, Mustafa DAĞTEKİN

Pages : 104-107

Doi:10.31590/ejosat.1225896

View : 12 | Download : 10

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor insert ignore into journalissuearticles values(k-nn);, support vector machine insert ignore into journalissuearticles values(SVM);, decision tree insert ignore into journalissuearticles values(DT);, and Gaussian Naive Bayes insert ignore into journalissuearticles values(GNB); machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree insert ignore into journalissuearticles values(DT); performs the best performance in terms of accuracy.
Keywords : Violence detection, machine learning, sociodemographic, classification, prediction