Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning

Abstract

This thesis deals with the problem of negative behaviour due to changes in mental and physical stress in adolescence. In particular, it is a study to solve the health care problem of students exposed to violence. Among the problematic behaviours, students exposed to violence, especially, have many problems with healthcare. A projective test using pictures can elicit information from adolescents through direct experiences represented by pictures to which the subject unconsciously reacts. Few methods analyse images drawn by adolescents as image data. This study analyses data from 134 violent students who received fifth-degree punishment for violent behaviour and 134 nonviolent students. We use the convolutional neural network (CNN)(softmax), CNN (support vector machine (SVM)), with the style transfer generative adversarial network, and ensemble techniques to analyse drawn images using WZT and predict violence through deep learning. We predict violence from pictures with an accuracy of 93%–98%. This study is the first to automatically analyze and predict violence with a deep learning model in images drawn by adolescents on WZT. It also features WZT to proactively conduct violence investigations to improve health care for students. Advances in deep learning for image feature extraction are expected to provide more research opportunities.

KEYWORDS:

• Wartegg–ZeichenTest
• deep learning-based violence prediction
• convolutional neural network (CNN) (softmax)
• CNN (SVM)
• healthcare

SUBJECT CLASSIFICATION CODES:

• Artificial intelligence
• deep learning
• healthcare

Acknowledgement

Acknowledgements, avoiding identifying any of the authors prior to peer review

Disclosure statement

The authors of this paper declare that they have no competing interests, financial or non-financial, to report.

Additional information

Funding

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2020-0-01789) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and this work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00254592) grant funded by the Korea government (MSIT).