StrokeSense is a data visualization dashboard that helps analyze the correlation between stroke symptoms and stroke risk. It provides interactive charts and a predictive model to assess stroke risk based on user input.

• Inspiration
• Features
• How It Works
• Technology Stack
• Setup and Installation
• Usage
• Screenshots
• Challenges Faced
• Future Improvements
• Contributors
• License

Stroke is a leading cause of death and disability worldwide. Early detection of stroke risk can save lives, and we wanted to create a data-driven solution that helps visualize and predict stroke risk based on symptoms and age.

• Interactive Heatmap: Shows the correlation between symptoms and stroke risk.
• Boxplots: Compare the risk of stroke for users with vs. without symptoms.
• Scatter Plot: Displays the distribution of stroke risk across different ages.
• Line Plot: Shows symptom distribution by risk category.
• Predictive Model: Accepts user input (age and symptoms) to predict stroke risk as either high or low.

1. Data Collection: We use a dataset containing stroke risk factors and symptoms.
2. Data Processing: The dataset is cleaned and preprocessed using Python.
3. Machine Learning Model: A RandomForestClassifier is trained to predict stroke risk based on user-provided symptoms and age.
4. Visualization Dashboard: Built using React and Plotly for intuitive data exploration.
5. API Backend: FastAPI serves predictions and provides data for visualizations.

• Frontend: React, Plotly
• Backend: FastAPI, Python, Pandas, NumPy, Scikit-learn
• Machine Learning: RandomForestClassifier
• Data Visualization: Plotly, Seaborn
• Hosting: Local development (future deployment to cloud services)

• Python 3.8+
• Node.js and npm

1. Clone the repository:

   git clone https://github.com/yourusername/strokesense.git
   cd strokesense/backend

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # For Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the FastAPI server:

   uvicorn app:app --reload

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start the React development server:

   npm start

• Visit http://localhost:3000 to access the StrokeSense dashboard.
• Enter age and symptoms to receive a stroke risk prediction.
• Explore the interactive visualizations to understand risk factors better.

• Finding a reliable dataset covering diverse stroke-related symptoms.
• Optimizing model performance while ensuring fast API response times.
• Designing an intuitive and informative dashboard layout.
• Ensuring seamless integration between React and FastAPI.

• Enhanced Model: Incorporate additional factors like lifestyle, diet, and medical history.
• Improved UI: Add more interactivity and better user feedback.
• Deployment: Host the project on a cloud platform for wider accessibility.
• Collaboration: Work with healthcare professionals to refine the model and insights.

• Shivam Singh - Lead Developer
• Ishan Jain - Developer

This project is licensed under the MIT License - see the LICENSE file for details.