A machine learning approach to detect depression and anxiety using supervised learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.

Bibliographic Details
Main Authors:	Ullas, Md Tahmidur Rahman, Begom, Mariyam, Ahmed, Anamika, Sultana, Raihan
Other Authors:	Alam, Md. Ashraful
Format:	Thesis
Language:	English
Published:	BRAC University 2019
Subjects:	Machine learning Depression Anxiety Support vector machine Linear regression Data mining. Machine learning.
Online Access:	http://hdl.handle.net/10361/12274

id	10361-12274
record_format	dspace
spelling	10361-122742022-01-26T10:15:43Z A machine learning approach to detect depression and anxiety using supervised learning Ullas, Md Tahmidur Rahman Begom, Mariyam Ahmed, Anamika Sultana, Raihan Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Machine learning Depression Anxiety Support vector machine Linear regression Data mining. Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-35). Depression, a major depressive disorder and anxiety are common medical illness which cause several symptoms that a ect the way a person feels, thinks, and the way he/she acts. These disorders are not only hard to endure, but are also risk factors for heart disease, panic attacks, dementia, and thus causing severe distress and functional impairment.Overall, more than 50% of the general population in middle and high-income countries su ers from at least one of these mental disorder at some point in their lives. In order to detect these disorders at an early age, we have proposed a model that uses a standard psychological assessment and machine learning algorithms to diagnose the di erent levels of such mental disorders. In our proposed model we used ve di erent types of AI algorithms: Convolutional neural network, Support vector machine, Linear discriminant analysis, K Nearest Neighbor Classi er and Linear Regression on the two datasets of anxiety and depression. These algorithms are used to nd the severity level of anxiety and depression, a patient is going through. In this paper we compared the results of the ve algorithms on the two datasets separately on the basis of di erent measurement metrics. The proposed model achieves the highest accuracy of 96% for anxiety and 96.8% for depression using the CNN algorithm while its results were compared with the other ve algorithms we have used for our model. Md Tahmidur Rahman Ullas Raihan Sultana Mariyam Begom Anamika Ahmed B. Computer Science and Engineering 2019-06-30T05:19:34Z 2019-06-30T05:19:34Z 2019 2019-04 Thesis ID 15101038 ID 15101003 ID 15101052 ID 15101139 http://hdl.handle.net/10361/12274 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning Depression Anxiety Support vector machine Linear regression Data mining. Machine learning.
spellingShingle	Machine learning Depression Anxiety Support vector machine Linear regression Data mining. Machine learning. Ullas, Md Tahmidur Rahman Begom, Mariyam Ahmed, Anamika Sultana, Raihan A machine learning approach to detect depression and anxiety using supervised learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
author2	Alam, Md. Ashraful
author_facet	Alam, Md. Ashraful Ullas, Md Tahmidur Rahman Begom, Mariyam Ahmed, Anamika Sultana, Raihan
format	Thesis
author	Ullas, Md Tahmidur Rahman Begom, Mariyam Ahmed, Anamika Sultana, Raihan
author_sort	Ullas, Md Tahmidur Rahman
title	A machine learning approach to detect depression and anxiety using supervised learning
title_short	A machine learning approach to detect depression and anxiety using supervised learning
title_full	A machine learning approach to detect depression and anxiety using supervised learning
title_fullStr	A machine learning approach to detect depression and anxiety using supervised learning
title_full_unstemmed	A machine learning approach to detect depression and anxiety using supervised learning
title_sort	machine learning approach to detect depression and anxiety using supervised learning
publisher	BRAC University
publishDate	2019
url	http://hdl.handle.net/10361/12274
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A machine learning approach to detect depression and anxiety using supervised learning

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