Risk2Reality

Inspiration

Mental health disorders often develop silently before becoming critical. We wanted to shift mental healthcare from reactive treatment to early prediction and prevention.

What it does

Risk2Reality analyzes psychological and behavioral patterns to forecast future mental health risks. It predicts 3-month depression risk, burnout probability, relapse likelihood, and generates a personalized prevention plan.

How we built it

We trained a machine learning model on psychological and behavioral indicators such as sleep patterns, anxiety scores, social isolation, and cognitive distortions. We then integrated the model into a web interface that simulates future mental health risk scenarios.

Challenges we ran into

Balancing prediction accuracy with limited dataset quality was difficult. Another challenge was translating clinical indicators into meaningful risk simulations.

Accomplishments that we're proud of

We successfully transformed psychological data into a predictive health simulation model. Our system moves beyond classification to actionable prevention insights.

What we learned

We learned how predictive modeling can be applied to mental health risk forecasting. We also gained experience in model optimization, evaluation metrics, and responsible AI design.

What's next for Risk2Reality

We plan to improve model accuracy using larger clinical datasets. Next, we aim to collaborate with mental health professionals to validate and refine the prevention recommendations.

Built With

• api
• collab
• css
• flask
• git
• github
• html
• javascript
• matplotlib
• numpy
• pandas
• python
• scikit-learn