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breast-cancer-africa-adjusted-dataset

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breast-cancer
african-physiognomy
healthcare-bias
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oncology
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metadata 
license: cc-by-4.0
task_categories:
  - tabular-classification
language:
  - en
tags:
  - medical
  - breast-cancer
  - african-physiognomy
  - healthcare-bias
  - representation-bias
  - oncology
size_categories:
  - n<1K

Breast Cancer Wisconsin Dataset: African Physiognomy Adjusted

Dataset Description

This dataset addresses representation bias in medical AI by providing an African physiognomy-adjusted version of the classic Wisconsin Breast Cancer Dataset. The adjustment methodology systematically modifies cellular morphology features to better reflect documented physiological differences in African populations.

Dataset Summary

  • Original Dataset: Wisconsin Breast Cancer Dataset (569 samples)
  • Adjusted Dataset: African physiognomy-adjusted version (569 samples)
  • Features: 30 morphological features from Fine Needle Aspirate (FNA) samples
  • Classes: Malignant (M) and Benign (B)
  • Adjustment Focus: 212 malignant cases modified based on African physiognomy patterns

Key Adjustments Applied

  1. Higher Breast Density: 5-8% increase in radius, area, and texture features
  2. Enhanced Irregularity: 12-19% increase in concavity, concave points, and fractal dimension
  3. Reduced Boundary Smoothness: 10-12% decrease in smoothness and symmetry features

Dataset Structure 

breast_cancer_original.csv          # Original Wisconsin dataset
breast_cancer_african_adjusted.csv  # African physiognomy-adjusted dataset

Features

Each dataset contains 32 columns:

  • id: Sample identifier
  • diagnosis: M (Malignant) or B (Benign)
  • 30 morphological features organized as:
    • Size/Density: radius, area, perimeter (mean, se, worst)
    • Texture: texture variability (mean, se, worst)
    • Irregularity: concavity, concave points, fractal dimension (mean, se, worst)
    • Boundary: smoothness, symmetry (mean, se, worst)
    • Compactness: compactness (mean, se, worst)

Usage

Loading the Dataset 

from datasets import load_dataset

# Load original Wisconsin version
original_dataset = load_dataset("electricsheepafrica/breast-cancer-african-adjusted", "wisconsin_breast_cancer_dataset")

# Access the data
original_dataset = original_dataset['train']  # Original Wisconsin dataset

Example Usage 

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load the adjusted dataset
df = pd.read_csv("breast_cancer_african_adjusted.csv")

# Prepare features and target
X = df.drop(['id', 'diagnosis'], axis=1)
y = df['diagnosis']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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

# Evaluate
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.3f}")

Methodology

Physiognomy-Based Adjustments

The adjustment methodology is based on documented physiological differences in African populations:

  • Higher Breast Density: African women demonstrate significantly higher mammographic breast density
  • Younger Onset: Breast cancer incidence peaks in the 40s vs 60s in Caucasian populations
  • Aggressive Subtypes: 20-40% triple-negative breast cancer rate vs 10-15% in Caucasian populations
  • Genetic Variants: Higher prevalence of BRCA1/2 mutations affecting cellular morphology

Statistical Approach

  • Adjustments applied exclusively to malignant cases (n=212)
  • Random multipliers drawn from normal distributions
  • Literature-informed scaling factors with biological variation
  • Maintained feature correlations and statistical validity

Validation and Limitations

Validation Framework

  • Internal Validation: Statistical consistency and biological plausibility maintained
  • External Validation Required: Clinical validation through prospective studies in African healthcare settings

Limitations

  • Synthetic adjustments based on literature review, not direct measurement
  • Limited to morphological features (excludes genetic/molecular markers)
  • Single adjustment model may not capture full population diversity
  • Requires validation against real African datasets

Ethical Considerations

  • Risk of perpetuating stereotypes if not properly validated
  • Importance of community engagement in African healthcare settings
  • Need for local data ownership and governance
  • Interim solution while working toward comprehensive data collection

Citation

If you use this dataset, please cite:

@misc{udodi2025breast,
  title={Addressing Representation Bias in Breast Cancer Datasets: A Physiognomy-Informed Approach for African Populations},
  author={Kossiso Udodi Royce},
  year={2025},
  publisher={Electric Sheep Africa},
  url={https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted}
}

Original Dataset Citation 

@misc{street1993nuclear,
  title={Nuclear feature extraction for breast tumor diagnosis},
  author={Street, W Nick and Wolberg, William H and Mangasarian, Olvi L},
  booktitle={IS\&T/SPIE International Symposium on Electronic Imaging: Science and Technology},
  volume={1905},
  pages={861--870},
  year={1993}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Contact

For questions or collaborations:

Acknowledgments

  • Original Wisconsin Breast Cancer Dataset creators

Disclaimer: This synthetic dataset is intended for research purposes to highlight representation bias in medical AI. Clinical validation through prospective studies in African healthcare settings is essential before any clinical application.