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"""Breast Cancer Wisconsin Dataset: African Physiognomy Adjusted""" |
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import csv |
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import datasets |
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_CITATION = """\ |
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@misc{udodi2025breast, |
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title={Addressing Representation Bias in Breast Cancer Datasets: A Physiognomy-Informed Approach for African Populations}, |
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author={Kossiso Udodi Royce}, |
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year={2025}, |
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publisher={Electric Sheep Africa}, |
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url={https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset addresses representation bias in medical AI by providing an African physiognomy-adjusted |
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version of the classic Wisconsin Breast Cancer Dataset. The adjustment methodology systematically |
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modifies cellular morphology features to better reflect documented physiological differences in |
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African populations. |
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Key adjustments include: |
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- Higher breast density (5-8% increase in size/texture features) |
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- Enhanced irregularity (12-19% increase in concavity/fractal features) |
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- Reduced boundary smoothness (10-12% decrease in smoothness/symmetry) |
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The dataset contains 569 samples with 30 morphological features from Fine Needle Aspirate (FNA) |
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samples, classified as Malignant (M) or Benign (B). |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted" |
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_LICENSE = "CC BY 4.0" |
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_URLS = { |
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"african_adjusted": "breast_cancer_african_adjusted.csv", |
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"wisconsin_breast_cancer_dataset": "breast_cancer_original.csv", |
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} |
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class BreastCancerAfricanAdjusted(datasets.GeneratorBasedBuilder): |
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"""Breast Cancer Wisconsin Dataset with African Physiognomy Adjustments""" |
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VERSION = datasets.Version("1.0.2") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="african_adjusted", |
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version=VERSION, |
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description="African physiognomy-adjusted breast cancer dataset", |
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), |
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datasets.BuilderConfig( |
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name="wisconsin_breast_cancer_dataset", |
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version=VERSION, |
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description="Original Wisconsin breast cancer dataset", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "african_adjusted" |
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def _info(self): |
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features = datasets.Features({ |
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"id": datasets.Value("float64"), |
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"diagnosis": datasets.Value("string"), |
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"radius_mean": datasets.Value("float64"), |
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"texture_mean": datasets.Value("float64"), |
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"perimeter_mean": datasets.Value("float64"), |
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"area_mean": datasets.Value("float64"), |
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"smoothness_mean": datasets.Value("float64"), |
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"compactness_mean": datasets.Value("float64"), |
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"concavity_mean": datasets.Value("float64"), |
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"concave points_mean": datasets.Value("float64"), |
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"symmetry_mean": datasets.Value("float64"), |
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"fractal_dimension_mean": datasets.Value("float64"), |
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"radius_se": datasets.Value("float64"), |
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"texture_se": datasets.Value("float64"), |
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"perimeter_se": datasets.Value("float64"), |
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"area_se": datasets.Value("float64"), |
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"smoothness_se": datasets.Value("float64"), |
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"compactness_se": datasets.Value("float64"), |
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"concavity_se": datasets.Value("float64"), |
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"concave points_se": datasets.Value("float64"), |
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"symmetry_se": datasets.Value("float64"), |
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"fractal_dimension_se": datasets.Value("float64"), |
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"radius_worst": datasets.Value("float64"), |
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"texture_worst": datasets.Value("float64"), |
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"perimeter_worst": datasets.Value("float64"), |
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"area_worst": datasets.Value("float64"), |
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"smoothness_worst": datasets.Value("float64"), |
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"compactness_worst": datasets.Value("float64"), |
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"concavity_worst": datasets.Value("float64"), |
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"concave points_worst": datasets.Value("float64"), |
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"symmetry_worst": datasets.Value("float64"), |
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"fractal_dimension_worst": datasets.Value("float64"), |
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}) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URLS[self.config.name] |
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data_file = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_file, |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding="utf-8") as f: |
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reader = csv.DictReader(f) |
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for key, row in enumerate(reader): |
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for field in row: |
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if field != "diagnosis": |
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try: |
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row[field] = float(row[field]) |
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except (ValueError, TypeError): |
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row[field] = None |
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yield key, row |
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