| 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.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.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.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 |
