Multimodal Prediction of Alzheimer's Disease

Overview

This project implements a multimodal approach for predicting Alzheimer's Disease using machine learning and deep learning techniques. It was developed as part of Washington University in St. Louis's CSE 419A: Introduction to AI for Health course. The project utilizes the OASIS-1 dataset to analyze various imaging and clinical data for early detection and prediction of Alzheimer's Disease.

Features

• Multimodal data integration from OASIS-1 dataset
• Deep learning models for image analysis
• Machine learning models for clinical data analysis
• Comprehensive performance evaluation metrics
• Visual analysis of model predictions
• Combined classifier leveraging both imaging and clinical data

Core Files

Notebooks and Analysis

• OASIS_1_Notebook_Aadarsha.ipynb - Main implementation notebook with data processing, model training, and evaluation

Documentation

• Report/neurips_2024.pdf - Detailed project report in NeurIPS format
• Final Demo - OASIS 1 - 419A.pptx - Project presentation slides

Visualizations

• Report/cnn_model.png - CNN architecture visualization
• Report/final_classifier.png - Final classifier architecture and results

Technologies

• Python 3.12
• TensorFlow/Keras
• PyTorch
• Scikit-learn
• XGBoost
• Pandas & NumPy
• Matplotlib & Seaborn
• Jupyter Notebooks

Getting Started

1. Set up the environment:

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Data Preparation:

   • Download the OASIS-1 dataset
   • Place the dataset in the appropriate directory
   • Follow the data preprocessing steps in the notebook

3. Running the Analysis:

   • Open OASIS_1_Notebook_Aadarsha.ipynb in Jupyter Notebook
   • Follow the notebook cells sequentially for complete analysis

Dependencies

Key dependencies include:

• tensorflow (2.18.0)
• torch (2.5.1)
• scikit-learn
• xgboost (2.1.3)
• numpy (2.0.2)
• pandas
• matplotlib (3.9.2)
• seaborn

Full dependencies are listed in requirements.txt

Project Structure

├── OASIS_1_Notebook_Aadarsha.ipynb    # Main implementation notebook
├── requirements.txt                    # Project dependencies
├── Report/                            # Project documentation
│   ├── neurips_2024.pdf              # Detailed project report
│   ├── cnn_model.png                 # Model architecture visualization
│   └── final_classifier.png          # Final results visualization
└── Final Demo - OASIS 1 - 419A.pptx  # Presentation slides

Acknowledgments

• Developed for CSE 419A at Washington University in St. Louis
• OASIS-1 Dataset contributors
• Course instructors and teaching assistants