🧠 Alzheimer's Disease Expert System

This project implements an AI-powered expert system designed to assist in the early detection and analysis of Alzheimer's Disease. Built using Python (Jupyter Notebook) and a clinical dataset, the system uses logical rules and medical indicators to make data-driven predictions and suggestions.

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📁 Project Files

• Alzheimers Disease Expert System.ipynb – Main implementation notebook
• alzheimers_disease_dataset.csv – Medical dataset used for analysis and decision support

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🚀 Features

• 📊 Dataset-driven insights from real Alzheimer’s patient data
• 🧠 Expert system rules to emulate human medical decision-making
• 💡 Early-stage prediction based on cognitive, demographic, and health indicators
• 🖥️ Fully implemented in Python using Pandas, NumPy, and rule-based logic
• 📈 Exploratory Data Analysis (EDA) included for deeper understanding

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📌 Dataset Overview

The dataset includes:

• Patient age, gender, MMSE scores
• Socio-demographic & clinical test results
• Diagnostic labels (e.g., demented, non-demented)

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🛠️ How to Run

1. Clone the repository:

   git clone https://github.com/your-username/alzheimers-expert-system.git
   cd alzheimers-expert-system

2. Open the Jupyter Notebook:

   jupyter notebook "Alzheimers Disease Expert System.ipynb"

3. Run all cells to interact with the rule-based diagnostic system.

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✅ Requirements

• Python 3.x
• Jupyter Notebook
• pandas, numpy, sklearn (install via pip install -r requirements.txt if provided)

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📚 Use Case

This project is ideal for:

• Healthcare analytics students
• Medical AI researchers
• Early diagnosis system developers
• Educational demonstrations

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🤝 Contributing

Pull requests and forks are welcome! Please feel free to improve the rule base, add visualizations, or enhance the dataset.

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📄 License

This project is open-source and available under the MIT License.