Identifying Children's Personality Styles through Drawing Analysis using Machine Learning

Abstract

The impact of personality styles on individuals' behavior and choices is undeniable. With the advancements in datamining and machine learning techniques, various methods have been proposed to automatically identify personality styles by processing extensive data and exploring some human behavioral patterns. In this article, we examine children's personality styles by analyzing their drawings using some machine learning techniques. We employ the Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire results to train k-nearest neighbors, random forest, and decision tree classifiers. Although we focus on a limited set of drawing features in this study, the results obtained are remarkably significant. We believe that the findings of this study will have a profound impact on early identification of children's personality styles and their decision-making throughout their lives.

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