Requirements

• Python 3.x
• Streamlit
• Pandas
• XGBoost

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501 to interact with the application.

Application Details

• The application uses a pre-trained XGBoost model stored in xgb_model3.json.
• The user inputs various features related to health and lifestyle, which are then used to predict the risk of Alzheimer's disease.

Model Features

The model expects the following features:

• Age
• Education Level
• BMI
• Diabetes
• Hypertension
• Cholesterol Level
• Family History of Alzheimer’s
• Cognitive Test Score
• Depression Level
• Sleep Quality
• Air Pollution Exposure
• Genetic Risk Factor (APOE-ε4 allele)
• Social Engagement Level
• Income Level
• Stress Levels
• Gender_Male
• Physical Activity Level_1
• Physical Activity Level_2
• Smoking Status_Former
• Smoking Status_Never
• Alcohol Consumption_Occasionally
• Alcohol Consumption_Regularly
• Dietary Habits_Healthy
• Dietary Habits_Unhealthy
• Employment Status_Retired
• Employment Status_Unemployed

Prediction

• The user inputs are converted into a DataFrame and reindexed to match the model columns.
• The data is then converted into a DMatrix for XGBoost.
• The model predicts the risk of Alzheimer's disease, and the result is displayed on the Streamlit interface.

License

This project is licensed under the MIT License.

Acknowledgements

• The application uses Streamlit for the web interface.
• The model is built using XGBoost.