Initial upload: African physiognomy-adjusted breast cancer dataset

README.md

@@ -2,6 +2,7 @@
 license: cc-by-4.0
 task_categories:
 - tabular-classification
+- medical
 language:
 - en
 tags:

dataset.py

@@ -0,0 +1,130 @@
+"""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