The Wisconsin Breast Cancer Dataset is one of the most popular resources in machine learning, but it has a major limitation: it mostly represents Caucasian women from the American Midwest. This makes it less reliable for African women, who often have different physiological traits that can affect diagnosis. To address this, we adjusted 30 features in 212 malignant cases from the dataset, using known differences in African populations, such as higher breast density, younger age of onset, and more aggressive cancer types. Our adjustments showed clear changes: 12 features went up (average 12.4%), 5 went down (average -10.8%). The biggest shifts were in concave points ( 18.9%), fractal dimension ( 12.5%), and smoothness (-12.3%), all of which reflect more irregular tumor shapes. This approach shows how we can adapt existing datasets to make them more inclusive, while also underscoring the urgent need to collect medical data directly from African populations. #MachineLearning #AIResearch #MedicalAI #HealthTech #DataScience #InclusiveAI #BiasInAI #FairData #MedicalDatasets #PopulationSpecificData #BreastCancerResearch #CancerDetection #AfricanHealthcare #GlobalHealth #HealthEquity #AIForGood #TechForHealth #DataForImpact #HealthcareInnovation #ResponsibleAI