An AI-assisted web application designed to support the early detection of childhood cancer through symptom-based assessment while promoting awareness with educational resources and interactive data visualization.
Note: This project was developed as a collaborative team project for academic purposes.
🌐 Live Demo: https://medicheck-india.surge.sh/
MediCheck India is an AI-assisted healthcare awareness platform that helps users perform a preliminary assessment of childhood cancer symptoms. The platform combines a simple symptom-based prediction system with awareness content and an interactive analytics dashboard to encourage early detection and public awareness.
Disclaimer: This project is intended for educational and awareness purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment.
- 🧒 Childhood cancer symptom assessment
- 🤖 AI-assisted risk prediction
- 📚 Educational awareness about childhood cancer
- 📊 Interactive analytics dashboard
- 🗺️ India state-wise cancer visualization
- 📈 Charts and statistical insights
- 📱 Responsive and user-friendly interface
- 📞 Contact page for support and information
- HTML5
- CSS3
- JavaScript
- Python
- NumPy
- Pandas
- Plotly
- Dash
- Plotly Express
- BloodMNIST Dataset
- Sample Childhood Cancer Data
- Surge
MediaCheck/
│
├── index.html
├── prediction.html
├── awareness.html
├── contact.html
├── app.js
├── styles.css
├── dashboard.py
├── bloodmnist.npz
├── dataset/
├── images/
├── assets/
└── README.md
- Users open the MediCheck application.
- They enter the child's symptoms.
- The application evaluates the symptoms using predefined scoring logic.
- A preliminary risk assessment is generated.
- Users can explore awareness resources and healthcare information.
- The analytics dashboard provides visual insights into childhood cancer data.
The project includes an interactive dashboard built using Dash and Plotly that provides:
- India state-wise cancer visualization
- Statistical analysis
- Interactive charts
- Data insights
- Healthcare awareness visualizations
This project was developed as a collaborative team project. Team members contributed to different parts of the application, including frontend development, dashboard creation, data visualization, testing, and overall project integration.
- Contributed to the development and integration of project features.
- Assisted in frontend development using HTML, CSS, and JavaScript.
- Participated in implementing and testing the symptom prediction module.
- Contributed to debugging, testing, and deployment.
- Collaborated with team members using Git and GitHub.
User
│
▼
Frontend (HTML/CSS/JavaScript)
│
▼
Symptom Prediction Logic
│
▼
Risk Assessment Result
│
▼
Dashboard & Data Visualization
│
▼
User Awareness & Insights
- Deep Learning-based image classification
- Hospital recommendation system
- Doctor appointment booking
- User authentication
- Medical report upload
- Cloud database integration
- Real-time healthcare APIs
- Mobile application support
Clone the repository
git clone https://github.com/your-username/MediaCheck.git
Navigate to the project directory
cd MediaCheck
Run the frontend by opening index.html in your browser.
To launch the analytics dashboard:
python dashboard.py
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# 📄 License
This project is developed for educational and academic purposes.
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# 👤 Author
**Jalaganika Yarramsetti**
GitHub: https://github.com/24A31A0539
LinkedIn: https://www.linkedin.com/in/jalaganika-yarramsetti-ba506632b
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# ⭐ Support
If you found this project helpful, consider giving the repository a ⭐ on GitHub.