Predicting early depression in WZT drawing image based on deep learning

Abstract

Abstract When stress causes negative behaviours to emerge in our daily lives, it is important to intervene quickly and appropriately to control the negative problem behaviours. Questionnaires, a common method of information gathering, have the disadvantage that it is difficult to get the exact information needed due to defensive or insincere responses from subjects. As an alternative to these drawbacks, projective testing using pictures can provide the necessary information more accurately than questionnaires because the subject responds subconsciously and the direct experience expressed through pictures can be more accurate than questionnaires. Analysing hand‐drawn image data with the Wartegg Zeichen Test (WZT) is not easy. In this study, we used deep learning to analyse image data represented as pictures through WZT to predict early depression. We analyse the data of 54 people who were judged as early depression and 54 people without depression, and increase the number of people without depression to 100 and 500, and aim to study in unbalanced data. We use CNN and CNN‐SVM to analyse the drawing images of WZT's initial depression with deep learning and predict the outcome of depression. The results show that the initial depression is predicted with 92%–98% accuracy on the image data directly drawn by WZT. This is the first study to automatically analyse and predict early depression in WZT based on hand‐drawn image data using deep learning models. The extraction of features from WZT images by deep learning analysis is expected to create more research opportunities through the convergence of psychotherapy and Information and Communication Technology (ICT) technology, and is expected to have high growth potential.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.