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🩺 Liver Cirrhosis Analysis

📝 Project Overview

This project provides an in-depth analysis of Liver Cirrhosis using clinical data. The objective is to explore the relationship between various biomarkers (e.g., Bilirubin, Copper, Alkaline Phosphatase) and patient demographics (e.g., Age, Sex) to understand the progression of the disease.

🌟 Highlights:

• 🔍 Data Exploration: Visualizing clinical indicators of liver cirrhosis.
• 📊 Biomarker Comparisons: Analysis of biomarkers across different disease stages.
• 🧑‍🤝‍🧑 Gender Differences: Exploring male and female variations in disease progression.
• 🎂 Age Trends: Statistical insights into age-related disease patterns.
• 💡 Survival Insights: Predicting survival rates and the likelihood of liver transplant for advanced stages.

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📚 Table of Contents

1. 📥 Installation Instructions
2. 🛠️ Usage
3. 📊 Data Overview
4. 🧮 Analysis
   • 📋 Exploratory Data Analysis (EDA)
   • 📈 Statistical Analysis
5. 🎨 Visualizations
6. 👩‍💻 Contributors
7. 📜 License

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📥 Installation Instructions

✅ Requirements:

• Python 3.x
• Required Libraries:
  • 📦 pandas for data manipulation
  • 🎨 matplotlib and seaborn for visualizations
  • 🧮 scipy and numpy for statistical computations

🚀 Steps to Set Up:

1. Clone the Repository

   git clone https://github.com/Bushra-Butt-17/Liver-Cirrhosis-Analysis.git

2. Install Dependencies
   Navigate to the project directory and install all required libraries:

   cd Liver-Cirrhosis-Analysis
   pip install -r requirements.txt

3. Run the Project
   Open the Jupyter Notebook or Python scripts:

   jupyter notebook

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🛠️ Usage

This project serves researchers, data scientists, and healthcare professionals by enabling them to:

• Examine correlations between biomarkers and liver disease progression.
• Predict outcomes like liver transplants or mortality.
• Use the provided codebase for clinical or educational purposes.

🔧 How to Use:

1. Load the dataset (ensure the correct file path).
2. Follow the structured steps in the notebook for:
   • Data cleaning.
   • EDA.
   • Statistical analysis and visualizations.
3. Interpret the outputs to draw meaningful insights.

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📊 Data Overview

The dataset contains clinical information for liver cirrhosis patients. Key columns include:

🏷️ Feature	📝 Description
Age	Patient's age.
Sex	Gender (M/F).
Stage	Stage of liver cirrhosis (1 to 4).
Bilirubin	Serum Bilirubin levels (marker of liver function).
Copper	Blood Copper levels (relevant for cirrhosis).
Alk_Phos	Alkaline Phosphatase (enzyme indicating liver dysfunction).
Albumin	Protein levels (lower levels suggest liver damage).
SGOT	Serum Glutamic Oxaloacetic Transaminase (liver enzyme).
Platelets	Platelet count (typically lower in cirrhosis).
Prothrombin	Blood clotting time, impacted by liver function.

Sample Data:

Stage	Age	Sex	Bilirubin	Copper	Alk_Phos	SGOT	Albumin	Platelets	Prothrombin
1	45	M	2.0	100	150	80	3.5	300,000	11.2
2	60	F	3.5	150	220	100	3.2	280,000	10.8

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🧮 Analysis

📋 Exploratory Data Analysis (EDA)

EDA techniques include:

• Descriptive statistics: Summary measures (mean, median, etc.).
• Outlier detection.
• Correlation analysis.

Sample Code:

# Calculate average biomarkers by gender
df.groupby('Sex')[['Age', 'Bilirubin', 'Copper']].mean()

📈 Statistical Analysis

Advanced techniques to uncover patterns:

• T-tests: Comparing biomarker levels between two groups.
• ANOVA: Testing significant differences across multiple stages.
• Survival Analysis: Predicting outcomes like mortality or transplants.

Example:

from scipy import stats
stage_1 = df[df['Stage'] == 1]['Bilirubin']
stage_3 = df[df['Stage'] == 3]['Bilirubin']
t_stat, p_value = stats.ttest_ind(stage_1, stage_3)
print(f"T-statistic: {t_stat}, P-value: {p_value}")

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🎨 Visualizations

Visualizations include:

• 📊 Stacked Bar Chart: Gender distribution across cirrhosis stages.
• 🧮 Heatmap: Correlations between biomarkers.
• 📦 Boxplots: Variability of biomarkers by disease stage.

Sample Visualization Code:

import seaborn as sns
correlation_matrix = df[['Bilirubin', 'Copper', 'Alk_Phos']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Biomarker Correlation Heatmap')
plt.show()

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👩‍💻 Contributors

Feel free to contribute by:

1. Reporting issues.
2. Opening pull requests.
3. Enhancing the analysis or visualizations.

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📜 License

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

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🏷️ Tags:

Python Data Analysis Visualization Medical Research Statistical Analysis Seaborn Matplotlib Pandas