| """Breast Cancer Wisconsin Dataset: African Physiognomy Adjusted""" |
|
|
| import csv |
| import datasets |
|
|
| _CITATION = """\ |
| @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} |
| } |
| """ |
|
|
| _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. |
| |
| Key adjustments include: |
| - Higher breast density (5-8% increase in size/texture features) |
| - Enhanced irregularity (12-19% increase in concavity/fractal features) |
| - Reduced boundary smoothness (10-12% decrease in smoothness/symmetry) |
| |
| The dataset contains 569 samples with 30 morphological features from Fine Needle Aspirate (FNA) |
| samples, classified as Malignant (M) or Benign (B). |
| """ |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted" |
|
|
| _LICENSE = "CC BY 4.0" |
|
|
| _URLS = { |
| "african_adjusted": "breast_cancer_african_adjusted.csv", |
| "wisconsin_breast_cancer_dataset": "breast_cancer_original.csv", |
| } |
|
|
| class BreastCancerAfricanAdjusted(datasets.GeneratorBasedBuilder): |
| """Breast Cancer Wisconsin Dataset with African Physiognomy Adjustments""" |
|
|
| VERSION = datasets.Version("1.1.0") |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( |
| name="african_adjusted", |
| version=VERSION, |
| description="African physiognomy-adjusted breast cancer dataset", |
| ), |
| datasets.BuilderConfig( |
| name="wisconsin_breast_cancer_dataset", |
| version=VERSION, |
| description="Original Wisconsin breast cancer dataset", |
| ), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "african_adjusted" |
|
|
| def _info(self): |
| features = datasets.Features({ |
| "id": datasets.Value("float64"), |
| "diagnosis": datasets.Value("string"), |
| "radius_mean": datasets.Value("float64"), |
| "texture_mean": datasets.Value("float64"), |
| "perimeter_mean": datasets.Value("float64"), |
| "area_mean": datasets.Value("float64"), |
| "smoothness_mean": datasets.Value("float64"), |
| "compactness_mean": datasets.Value("float64"), |
| "concavity_mean": datasets.Value("float64"), |
| "concave points_mean": datasets.Value("float64"), |
| "symmetry_mean": datasets.Value("float64"), |
| "fractal_dimension_mean": datasets.Value("float64"), |
| "radius_se": datasets.Value("float64"), |
| "texture_se": datasets.Value("float64"), |
| "perimeter_se": datasets.Value("float64"), |
| "area_se": datasets.Value("float64"), |
| "smoothness_se": datasets.Value("float64"), |
| "compactness_se": datasets.Value("float64"), |
| "concavity_se": datasets.Value("float64"), |
| "concave points_se": datasets.Value("float64"), |
| "symmetry_se": datasets.Value("float64"), |
| "fractal_dimension_se": datasets.Value("float64"), |
| "radius_worst": datasets.Value("float64"), |
| "texture_worst": datasets.Value("float64"), |
| "perimeter_worst": datasets.Value("float64"), |
| "area_worst": datasets.Value("float64"), |
| "smoothness_worst": datasets.Value("float64"), |
| "compactness_worst": datasets.Value("float64"), |
| "concavity_worst": datasets.Value("float64"), |
| "concave points_worst": datasets.Value("float64"), |
| "symmetry_worst": datasets.Value("float64"), |
| "fractal_dimension_worst": datasets.Value("float64"), |
| }) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| urls = _URLS[self.config.name] |
| data_file = dl_manager.download_and_extract(urls) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": data_file, |
| "split": "train", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath, split): |
| with open(filepath, encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for key, row in enumerate(reader): |
| |
| for field in row: |
| if field != "diagnosis": |
| try: |
| row[field] = float(row[field]) |
| except (ValueError, TypeError): |
| row[field] = None |
| |
| yield key, row |
|
|
