CYTOLOGICAL FEATURE-BASED MACHINE LEARNING CLASSIFICATION OF BENIGN AND MALIGNANT BREAST TUMORS USING THE WISCONSIN DIAGNOSTIC BREAST CANCER
Abstract
Breast cancer is still a serious worldwide health issue, and prompt diagnosis and treatment planning depend on the ability to distinguish between benign and malignant tumors. This study aimed to classify benign and malignant breast tumors using cytological through supervised machine learning techniques. The dataset contained 569 tumor records and 30 numerical cytological features derived from breast tumor cell nuclei. After removing non-informative columns, the diagnosis variable was encoded into benign and malignant classes, and the numerical features were standardized. Six
ML models were applied: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROCAUC. The descriptive results showed that malignant tumors had higher mean values for radius, perimeter, area, concavity, and concave points, indicating greater nuclear size and structural irregularity. The classification results showed strong performance across all models. Random Forest and Support Vector Machine achieved the highest accuracy of 0.9737 and F1-score of 0.9630, while LR achieved the highest ROC-AUC value of 0.9960. These findings indicate that cytological
features provide strong diagnostic separation between benign and malignant tumors. The study concludes that ML models can serve as effective decision-support tools for breast tumor classification, although external validation is required before clinical application.
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