Automatic depression detection

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dc.contributor.author Tusiime, Hewitt
dc.contributor.author Nahabwe, Alvin
dc.contributor.author Babirye, Grace
dc.contributor.author Kimuli, Wasajja Julius
dc.date.accessioned 2022-05-12T08:50:51Z
dc.date.available 2022-05-12T08:50:51Z
dc.date.issued 2022-01
dc.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. en_US
dc.description.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. en_US
dc.identifier.citation Tusiime, H., et al. (2022). Automatic depression detection. Undergraduate dissertation. Makerere University en_US
dc.identifier.uri http://hdl.handle.net/20.500.12281/12406
dc.language.iso en en_US
dc.publisher Makerere University en_US
dc.subject Depression en_US
dc.subject Mental health en_US
dc.subject Automatic depression detection en_US
dc.subject Acoustic features en_US
dc.subject Machine learning en_US
dc.title Automatic depression detection en_US
dc.type Thesis en_US
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