Violence Prediction on Somatization and Emotional Self Awareness with Machine Learning Methods
DOI:
https://doi.org/10.17986/blm.1385Keywords:
violence, somatization, emotional self awareness, machine learningAbstract
This study is intended to predict the violent victimization of individuals through the classification algorithms of supervised learning, one of the methods of machine learning through somatization and emotional self-awareness concepts, and 149 (27%) male and It consists of a total of 552 participants, including 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 the study. K-Nearest Neighbor, support vector machines, Naive Bayes and logistics regression were used, one of the classification algorithms frequently used in machine learning; 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 estimated at a certain rate of accuracy of the victimization of violence of individuals.
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