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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    A machine learning approach to detect depression and anxiety using supervised learning

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    15101038,15101038,15101139,15101052_CSE.pdf (2.851Mb)
    Date
    2019-04
    Publisher
    BRAC University
    Author
    Ullas, Md Tahmidur Rahman
    Begom, Mariyam
    Ahmed, Anamika
    Sultana, Raihan
    Metadata
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    URI
    http://hdl.handle.net/10361/12274
    Abstract
    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.
    Keywords
    Machine learning; Depression; Anxiety; Support vector machine; Linear regression
     
    LC Subject Headings
    Data mining.; Machine 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.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 33-35).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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