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A Fast and Interpretable Logistic Regression Framework for Breast Tumor Classification Using the Wisconsin Diagnostic Dataset
2025-12-31
oncology
Title + abstract only
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Early and reliable discrimination between malignant and benign breast tumors is essential for clinical decision-making and for reducing unnecessary invasive procedures. This study presents a lightweight and reproducible machine-learning pipeline that integrates standard feature normalization with logistic regression to classify breast tumors using the Breast Cancer Wisconsin (Diagnostic) dataset (WDBC), which contains 569 samples described by 30 quantitative features derived from digitized fine-...
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