Automatic depression detection
Automatic depression detection
Date
2022-01
Authors
Tusiime, Hewitt
Nahabwe, Alvin
Babirye, Grace
Kimuli, Wasajja Julius
Journal Title
Journal ISSN
Volume Title
Publisher
Makerere University
Abstract
Depression is a common mental disorder that affects more than 264 million people worldwide. Between 76% and 85% of people in low and middle-income countries receive no treatment for their disorder. There are many barriers to effective treatment such as social stigma, lack of resources, shortage of trained professionals to mention but a few. The purpose of this study was to investigate how machine learning algorithms can be used to create self-help applications that detect depression from vocal acoustic features and suggest remedies so as to bridge the treatment gap.
Description
A project report submitted to the School of Computing and Informatics Technology for the study leading to a project in partial fulfillment of the requirements for the award of the Degree of Bachelor of Science in Software Engineering of Makerere University.
Keywords
Depression,
Mental health,
Automatic depression detection,
Acoustic features,
Machine learning
Citation
Tusiime, H., et al. (2022). Automatic depression detection. Undergraduate dissertation. Makerere University