PsycheScan

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  Assessment

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Inspiration

Mental health is an essential aspect of our well-being, yet millions of people worldwide struggle with depression, anxiety, and stress without receiving the necessary care. Inspired by the need to make mental health assessments more accessible, I built PsycheScan—an AI-powered platform that provides real-time insights into mental health using validated psychological scales like DASS-21 and TIPI. My goal was to create a tool that empowers individuals to monitor their mental well-being and take action when needed.

What it does

PsycheScan is an AI-powered web platform designed to assess mental health by evaluating levels of depression, anxiety, and stress using the DASS-21 scale, while also providing insights into personality traits through the Ten-Item Personality Inventory (TIPI). Users simply answer a set of questions, and the system delivers real-time feedback on their mental health status, categorized into normal, mild, moderate, severe, or extremely severe.

How we built it

Frontend: Built using Next.js, providing a clean and responsive UI for users to complete the assessment. Backend: Powered by FastAPI, handling the data processing, managing the machine learning models, and delivering results in real-time. Machine Learning Models: Developed using Python and scikit-learn, these models analyze user responses to the DASS-21 and TIPI scales and deliver accurate results on mental health status. Deployment: The application is deployed on Vercel, ensuring a fast, scalable, and accessible platform.

Challenges we ran into

Data Processing: Integrating multiple psychological scales (DASS-42, TIPI) into the machine learning model and ensuring consistency in the results was a key technical challenge. Model Accuracy: Ensuring the machine learning models produced reliable and meaningful assessments based on user input required constant fine-tuning. UI Design: Creating an intuitive and non-intimidating user interface that users would feel comfortable interacting with for mental health assessments was a balancing act.

Accomplishments that we're proud of

Successfully built a functional prototype of PsycheScan, capable of delivering accurate mental health insights based on psychological assessments. Integrated multiple psychological scales into a single tool, providing users with a comprehensive mental health overview. Deployed the application on Vercel, making it accessible, fast, and reliable for users to use from anywhere. Ensured a seamless user experience with an intuitive and responsive design that caters to users’ mental health needs.

What we learned

Modeling Psychological Data: We gained valuable insights into building machine learning models for psychological assessments, learning how to process complex, subjective data and deliver meaningful results. Frontend-Backend Integration: The experience taught us the importance of smooth integration between the frontend and backend to ensure seamless data flow and real-time responses. User Experience: We learned how crucial it is to design an app that is not only functional but also reassuring and user-friendly, especially when dealing with mental health-related issues.

What's next for PsycheScan

AI Personalization: Introduce personalized recommendations and action plans based on users’ mental health results, offering resources or next steps to improve their well-being. Data Insights: Use aggregated data (while maintaining privacy) to provide insights into mental health trends and offer users a way to track their mental health over time.

Built With

• git
• github
• javascript
• javascript-frontend-framework:-next.js-(react-based)-backend-framework:-fastapi-machine-learning-libraries:-scikit-learn
• pandas
• python
• react
• scikit-learn
• vercel