electricsheepafrica/Africa-Cirrhosis-prediction-Dataset

This dataset contains clinical information about patients with primary biliary cholangitis (PBC). The data includes laboratory values, clinical features, and survival information for patients with this chronic liver disease.

Dataset Summary

• Total Samples: 418
• Features: 20
• Task: Survival analysis and prediction of disease progression
• Format: CSV/Parquet

Features

Patient Information

• ID: Patient identifier (integer)
• N_Days: Number of days between registration and the earliest of death, transplantion, or study analysis time (integer)
• Status: Patient status at last follow-up (C=censored, D=dead, CL=lost to follow-up) (categorical)
• Drug: Drug treatment (D-penicillamine or Placebo) (categorical)
• Age: Age in days (integer)
• Sex: Sex (M=male, F=female) (categorical)

Clinical Features

• Ascites: Presence of ascites (Y/N) (binary)
• Hepatomegaly: Presence of hepatomegaly (Y/N) (binary)
• Spiders: Presence of spider angiomata (Y/N) (binary)
• Edema: Presence of edema (N=none, S=edema without diuretics, Y=edema despite diuretics) (categorical)
• Stage: Histologic stage of disease (1-4) (ordinal)

Laboratory Values

• Bilirubin: Serum bilirubin (mg/dl) (continuous)
• Cholesterol: Serum cholesterol (mg/dl) (continuous)
• Albumin: Serum albumin (g/dl) (continuous)
• Copper: Urine copper (μg/day) (continuous)
• Alk_Phos: Alkaline phosphatase (U/liter) (continuous)
• SGOT: Serum glutamic-oxaloacetic transaminase (U/ml) (continuous)
• Tryglicerides: Triglycerides (mg/dl) (continuous)
• Platelets: Platelet count (continuous)
• Prothrombin: Prothrombin time (seconds) (continuous)

Dataset Statistics

Status Distribution

• C: 53.8%
• CL: 6.0%
• D: 40.2%

Sex Distribution

• M: 59.8%
• F: 40.2%

Age Statistics (years)

• Range: 21.0 - 72.5 years
• Mean ± SD: 45.7 ± 10.4 years
• Median: 46.1 years

Disease Stage Distribution

• Stage 1: 1.7%
• Stage 2: 18.7%
• Stage 3: 39.2%
• Stage 4: 40.4%

Clinical Features Prevalence

• Ascites: 80.7% absent19.3% present,
• Hepatomegaly: 71.4% present, 28.6% absent
• Spiders: 56.0% absent44.0% present,
• Edema: 70.8% none, 19.9% without diuretics, 9.3% with diuretics

Laboratory Values Summary Statistics

Feature	Mean ± SD	Min	25%	50%	75%	Max
Bilirubin	3.8 ± 5.2	0.4	0.9	1.6	4.1	33.5
Cholesterol	369.5 ± 231.9	120.0	249.5	309.5	400.0	1775.0
Albumin	3.2 ± 0.4	1.8	2.9	3.2	3.4	4.5
Copper	97.6 ± 85.6	4.0	41.2	73.0	123.0	588.0
Alk_Phos	2585.9 ± 2858.6	372.6	1136.8	1649.4	2600.8	18383.3
SGOT	172.4 ± 83.9	32.6	114.9	155.9	212.3	766.6
Tryglicerides	124.7 ± 65.1	33.0	84.2	108.0	151.0	598.0
Platelets	205.8 ± 83.2	48.2	143.4	200.8	252.4	538.2
Prothrombin	11.8 ± 1.3	9.4	10.9	11.6	12.3	20.0

Usage

Loading the Dataset

import pandas as pd

# Load the dataset
df = pd.read_csv('cirrhosis_african_adjusted.csv')

# Display basic information
print(f"Dataset shape: {df.shape}")
print(f"Columns: {list(df.columns)}")

Survival Analysis Example

import pandas as pd
import matplotlib.pyplot as plt
from lifelines import KaplanMeierFitter

# Load the dataset
df = pd.read_csv('cirrhosis_african_adjusted.csv')

# Create Kaplan-Meier survival curves by treatment group
kmf = KaplanMeierFitter()
plt.figure(figsize=(10, 6))

for name, group in df.groupby('Drug'):
    # Skip if group is empty or has too few samples
    if len(group) > 0:
        kmf.fit(
            durations=group['N_Days'],
            event_observed=(group['Status'] == 'D').astype(int),
            label=name
        )
        kmf.plot_survival_function()

plt.title('Kaplan-Meier Survival Curves by Treatment')
plt.xlabel('Days')
plt.ylabel('Survival Probability')
plt.grid(True)

Predictive Modeling Example

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Load and preprocess the data
df = pd.read_csv('cirrhosis_african_adjusted.csv')

# Create binary target (alive vs. dead)
df['Survived'] = (df['Status'] != 'D').astype(int)

# Select features
features = ['Age', 'Sex', 'Bilirubin', 'Albumin', 'Stage', 'Prothrombin']
X = pd.get_dummies(df[features], drop_first=True)
y = df['Survived']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train a model
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.3f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))