ABSTRACT

This study aims to predict the violent victimization of individuals using the classification of algorithms of supervised learning, one of the methods of machine learning through somatization and emotional self-awareness concepts.

This study consisted of 552 participants, 149 (27%) male and 403 (73%) women. Personal Information Form, Somatization Scale and An Emotional Self Awareness Scale-10 (A-DÖFÖ-10) were used as data collection tools in this study. K-Nearest Neighbor, Support Vector Machines, Naive Bayes and Logistics Regression, one of the classification algorithms frequently used in machine learning, were applied.

The performance comparison of the relevant classers was made according to the model performance criteria. Given accuracy and f1-score values, the best classification performance was derived from Logistics Regression with 0.74 accuracy and 0.82 f1-score value.

Accordingly, it is possible to say that the methods of machine learning through somatization and emotional self-awareness concepts can be used to estimate the victimization of violence of individuals at a certain rate of accuracy.

Keywords: Violence, Somatization, Emotional Self-Awareness, Machine Learning