| | """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 |
| |
|
