AI-driven Classification of Alzheimer's Disease and Mild Cognitive Impairment

Overview

This project employs advanced machine learning algorithms to classify Alzheimer's Disease with high accuracy. By leveraging a dataset of cognitive and biological markers, the code achieves remarkable performance metrics, including:

• Accuracy: >80%
• Sensitivity: >80%
• Specificity: >80%
• F1 Score: >80%

The implementation is designed for ease of use and reproducibility, enabling researchers and practitioners to effectively diagnose Alzheimer's Disease.

Key Features

• Implements a Multinomial Logistic Regression (MLR) & Multi-Layer Perceptron (MLP)
• Multi-class classification.
• Optimized hyperparameters for high performance.
• Outputs detailed evaluation metrics (accuracy, sensitivity, specificity, F1 score).
• Configurable for different datasets.

Model Summaries

MLR Summary

Layer (type)	Input Shape	Output Shape	Param #	Trainable
LogisticRegression	[8, 1, 237]	[8, 1, 3]	--	True
├─ Linear (linear)	[8, 1, 237]	[8, 1, 3]	714	True

Total Parameters: 714
Trainable Parameters: 714
Non-trainable Parameters: 0
Total Mult-Adds (M): 0.01

Resource	Size (MB)
Input size	0.01
Forward/backward pass size	0.00
Params size	0.00
Estimated Total Size	0.01

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MLP Summary

Layer (type)	Input Shape	Output Shape	Param #	Trainable
MLP	[8, 1, 237]	[8, 1, 3]	--	True
├─ Linear (fc1)	[8, 1, 237]	[8, 1, 64]	15,232	True
├─ Linear (fc2)	[8, 1, 64]	[8, 1, 64]	4,160	True
├─ Linear (fc3)	[8, 1, 64]	[8, 1, 32]	2,080	True
├─ Linear (fc4)	[8, 1, 32]	[8, 1, 32]	1,056	True
├─ Linear (fc5)	[8, 1, 32]	[8, 1, 3]	99	True

Total Parameters: 22,627
Trainable Parameters: 22,627
Non-trainable Parameters: 0
Total Mult-Adds (M): 0.18

Resource	Size (MB)
Input size	0.01
Forward/backward pass size	0.01
Params size	0.09
Estimated Total Size	0.11

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Requirements

To run this project, install the following dependencies:

• Python 3.8+
• Required libraries (install via requirements.txt):

  pip install -r requirements.txt

Dataset

The dataset used should include relevant features for Alzheimer's Disease classification. Ensure your dataset is in CSV format with appropriate preprocessing applied. The dataset template template has been added to the repository as Features-template.xlsx

Usage

Step 1: Clone the Repository

git clone https://github.com/taha-parsayan/Classification-of-Alzheimers-Disease-using-AI-algorithms.git
cd Classification-of-Alzheimers-Disease-using-AI-algorithms

Step 2: Prepare the Data

Place your dataset in the data/ directory. Modify the data loading section in main.py to specify your dataset path and adjust preprocessing steps as needed.

Step 3: Run the Code

Execute the script to train and evaluate the model:

python main.py

Step 4: View Results

The results, including accuracy, sensitivity, specificity, and F1 score, will be displayed in the console and saved to results/.

Code Structure

Classification-of-Alzheimers-Disease-using-AI-algorithms/
│
├── main.py                # Main script for training and evaluation
├── data/                  # Directory for the dataset
├── models/                # Directory for saving trained models
├── results/               # Directory for saving results
├── utils.py               # Utility functions (if applicable)
└── requirements.txt       # Required Python libraries

Performance Metrics

Metric	Value (%)
Accuracy	>80
Sensitivity	>80
Specificity	>80
F1 Score	>80

License

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

Author

Taha Parsayan
Ph.D. Student in AI
For questions or collaborations, contact: GitHub Profile

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The main.py file appears to implement a deep learning pipeline for classifying Alzheimer's Disease. The initial inspection shows that it uses PyTorch for model implementation, along with several libraries for data preprocessing, statistical analysis, and evaluation metrics.

Here’s how the README should be adjusted based on these observations:

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Classification of Alzheimer's Disease Using AI Algorithms

Overview

This project utilizes a PyTorch-based Multi-Layer Perceptron (MLP) to classify Alzheimer's Disease. The pipeline includes data preprocessing, statistical analysis, and robust evaluation metrics to ensure the model's effectiveness. With an emphasis on reproducibility and performance, this implementation achieves:

• Accuracy: >80%
• Sensitivity: >80%
• Specificity: >80%
• F1 Score: >80%

Key Features

• PyTorch implementation of MLP for multi-class classification.
• Preprocessing and feature selection included.
• Statistical validation using libraries such as scipy and pingouin.
• Automated evaluation with comprehensive metrics.

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