Date of Award
5-2026
Degree Type
Masters Thesis
Degree Name
Master of Science (MS)
School
Computing Sciences and Computer Engineering
Committee Chair
Dr. Chaoyang Zhang
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Dr. Nick Rahimi
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dr. Zhaoxian Zhou
Committee Member 3 School
Computing Sciences and Computer Engineering
Abstract
Suicide remains a leading cause of death among adolescents despite more access to healthcare information than ever before. Medical professionals struggle to make accurate diagnoses and catch warning signs with the overwhelming amount of data available. Machine learning algorithms, including neural networks, have previously been employed for this task, yet it remains an understudied domain.
This research aims to evaluate the capabilities of Multi-Layer Perceptron (MLP) and a selection of its successors, ResNet and MLP with a category embedding layer, at the task of predicting suicidal ideation among high-school students. This research finds ResNet to be the most capable at minimizing false negatives, but with a higher amount of false positives. Additionally, this research finds all three models generalize to prior versions of the dataset with great success.
Copyright
Kyle Brown, 2026
Recommended Citation
Brown, Kyle, "Evaluating Modern Neural Network Architectures for Suicide Prediction" (2026). Master's Theses. 1197.
https://aquila.usm.edu/masters_theses/1197