Datasets:
File size: 5,121 Bytes
91fdf7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | """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):
# Convert numeric fields
for field in row:
if field != "diagnosis":
try:
row[field] = float(row[field])
except (ValueError, TypeError):
row[field] = None
yield key, row
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