Mental Health Checkup using deep learning

Second Page(Name and Age Input)
Home Page
Third Screen(First Question about how you are feeling)
Survey Screen 1
Survey Screen 2
Suggestion Screen

Inspiration

Our inspiration for creating a depression detection application is that depression is considered taboo and mental health is not given enough importance as physical health, though we know how depression can cause people to commit suicide and after the pandemic, the mental health problem has increased drastically. So we thought this area is something we need to consider since it is the hour of the need, and we need to provide some solutions in this area and make people aware of it.

What it does?

Our application is based on detecting depression. The user starts the survey by filling in his/her name, then the user needs to fill a small interactive survey whose results will provide you a score which is being predicted by a deep learning model at the backend, and finally a score is generated and suggestions are provided to the user according to the score they get.

How we built it?

Ideation: Initially, we started with a brainstorming session for collecting different ideas and which idea can create maximum impact. We finalized the idea of depression detection and started to find some datasets if available any.

Exploration: Once we got the dataset, we divided the overall task into smaller tasks and assigned it to the team members. We mostly focused on creating a deep learning model, what colours and themes to use for our website, and at last which framework to use for creating an application.

Prototyping: We created a prototype in Figma so to know how our overall website will look and feel. It also helped in creating the coded website faster since we knew which element to use and where to use it.

Implementation: We created a deep learning neural network, coded our website, and built an application on the flask framework. We chose flask as it is a python framework, so we can directly add our model, and we also had some previous experience as well. We faced some problems while creating the application, which we will mention in the next section. At last, we deployed it as a flask application.

Challenges we ran into

The challenge we faced at the initial stage was to find a good dataset since the datasets available were not too good. After that, creating the flask application was a tough task as we need to take care of multiple things like adding the deep learning model, adding images with a given syntax. At last integration of front-end and back-end took some time, and we faced some issues, but we got over it.

Accomplishments that we're proud of:

Firstly, we are proud that we took mental health as a topic since no one talks about it that much, yet we decided to move forward with the topic. Secondly, we were able to create a flask application even after facing so many issues was really great and something to cheer about. Finally, the team working together to build the application in this online platform is something we are really proud of.

What we learned

Team-work
Time management skills
Improved deep learning skills
Learned how to systematically create flask application
Integrating back-end and front-end.

What's next for Mental Health Checkup using Deep Learning

In future, we would like to add more information about mental health so that user can be aware of mental health. We would also like to add a recommendation system which will recommend user videos, articles, or things related to mental health. Lastly, we will try to integrate a system through which a user can directly consult to a doctor or book an appointment.