Date of Completion

Spring 5-10-2019

Thesis Advisor(s)

Jinbo Bi

Honors Major

Computer Science and Engineering


Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing | Other Computer Engineering


Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In this analysis, we find those node features and edge metrics which lead to graph that accurately represents a depression data set on which GNN can achieve over 80% accuracy.