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Depression Detection System

Param Goel, Palak Kumari, Nimisha Pandey, Shivanand Mishra, Sweekriti Kesarwani

Abstract


Traditionally, depression was identified through in-depth clinical interviews, during which the psychologist would analyze the subject’s responses to ascertain his or her mental condition. With the prevalence of depression increasing, some automated and trustworthy methods of depression recognition are needed. Our Objective it to design a model that can extract relevant features automatically as an input using efficient machine learning algorithms and further detect the depressive state of the user as an output. By combining the three modalities-text, audio, and video—in our model, we attempt to emulate this strategy and predict an output related to the patient’s mental health. Our approaches include extracting visual, auditory, and text features information from a real time video and further analyze data to find any patterns that might indicate depression in the relevant users. It has additional features that make it simpler for users to use and collect data in an efficient manner. This is a cutting- edge platform created to gather multimodal data, assist the creation of prediction models for identifying critical health indicators, such as depression, and is practical to use with community members.


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DOI: https://doi.org/10.37628/jeset.v9i1.1850

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