Evaluating Modern Neural Network Architectures for Suicide Prediction

<p>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.</p> <p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2026

Author dc:creator

• Brown, Kyle

Contributors dc:contributor

• Dr. Chaoyang Zhang
• Dr. Nick Rahimi
• Dr. Zhaoxian Zhou

• Suicide Prediction
• Machine Learning
• Nueral Networks
• Multi-Layer Perceptron
• Artificial Intelligence and Robotics

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/1197

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-2291