Faculty Advisor

Rundensteiner, Elke A.

Faculty Advisor

Paffenroth, Randy Clinton

Abstract

Using audio and text data from multiple sources, we evaluated the viability of using machine and deep learning to identify depression and anxiety. Machine learning methods using sub-clip boosting achieved an F1 score of 0.81 for depression and 0.83 for anxiety. Our convolutional neural networks and long-term short term memory models achieved F1 scores of 0.55 and 0.68 respectively for depression. As feature engineering, we used topological data analysis to create Betti curves in our machine learning pipeline. Furthermore, we developed a pipeline to generate text messages with deep learning models, for data augmentation purposes.

Publisher

Worcester Polytechnic Institute

Date Accepted

2020-04-02

Major

Interdisciplinary

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

Advisor Department

Mathematical Sciences

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