Alvira - Early Alzheimer's Detection

Inspiration Alzheimer’s disease affects millions worldwide, often going undetected until it’s too late for effective intervention. We were inspired to create a solution that leverages technology to enable early detection, empowering individuals and healthcare providers to take timely action.

What it does Alvira is a web-based platform that uses machine learning models to analyze user data and predict the likelihood of early-stage Alzheimer’s. Users can input relevant information, and the system provides guidance, predictions, and resources to support proactive health management.

How we built it We built Alvira using a React frontend for a seamless user experience and a Python Flask backend to handle data processing and model inference. The backend integrates pre-trained models (SVM, PCA, label encoder, scaler) and audio processing for advanced analysis. The project also utilizes modern CSS frameworks for responsive design.

Challenges we ran into Integrating multiple machine learning models and ensuring compatibility between frontend and backend was challenging. Handling audio data and optimizing model performance for real-time predictions required significant debugging and testing. Ensuring user privacy and data security was also a key concern.

Accomplishments that we're proud of We successfully developed a functional prototype that delivers accurate predictions and a user-friendly interface. The integration of audio analysis and machine learning for health diagnostics is a unique achievement. We’re proud of the collaborative effort and the impact this project can have.

What we learned We learned about the complexities of deploying machine learning models in production environments, especially for sensitive health data. The importance of user experience, data privacy, and robust backend architecture became clear throughout development.

What's next for Alvira - Early Alzheimer's Detection We plan to expand Alvira’s capabilities by incorporating more data sources, refining prediction accuracy, and adding features for healthcare professionals. Future work includes mobile app development, clinical validation, and partnerships with medical organizations to maximize impact.

Built With

• css3
• deep-learning
• html5
• javascript
• machine-learning
• python
• react