Using Machine Learning to Analyze Children’s Drawings as Indicators of Mental Well-Being

Author

Nicholas R. Lawrie, University of San FranciscoFollow

Date of Graduation

Spring 5-21-2022

Document Type

Thesis

Degree Name

Master of Science in Applied Economics (MSAE)

College/School

College of Arts and Sciences

Department/Program

Economics

First Advisor

Bruce Wydick

Abstract

Human figure drawings are a well-studied diagnostic tool for emotional distress in children. Cleft lip/palate is one of the most common birth defects in the world, and has been shown to negatively impact emotional well-being in childhood which can have negative economic consequences in adulthood. Utilizing a dataset of human figure drawings from children in India and survey data on mental health, this paper will assess the impact of corrective surgery on mental health outcomes, as well as assess the validity of the drawing emotional indicators themselves. The results indicate that while the emotional indicators may not be valid in predicting emotional distress in this sample, there is a positive relationship between corrective surgery and mental health outcomes.

Recommended Citation

Lawrie, Nicholas R., "Using Machine Learning to Analyze Children’s Drawings as Indicators of Mental Well-Being" (2022). Master's Theses. 1416.
https://repository.usfca.edu/thes/1416