Аңдатпа
Breast cancer continues to be the most encountered malignancy in women globally and a leading cause of cancer-related mortality. This study describes an Information Technology approach to evaluate interpretable machine-learning methods for breast cancer prediction using routine clinical data and to situate performance against prior literature. All calculations are based on the Breast Cancer Wisconsin Diagnostic dataset (569 instances; malignant/benign labels) hosted by the UCI Machine Learning Repository. Each sample corresponds to a breast mass classified as malignant or benign. Four supervised machine learning models were applied: Logistic Regression with L1 penalty, Random Forest, Decision Tree, and Naïve Bayes, and compared the area under the ROC curve (AUC), accuracy, sensitivity, and specificity using DeLong’s test with Holm correction. The reproducible pipeline consisted of preprocessing, recursive feature elimination for feature selection, and a 5-fold cross-validation for hyperparameter tuning. Among the four models, the L1-penalized Logistic Regression yielded the best results, with an AUC indicating accuracy, sensitivity, and specificity of 99.6% (97.3%, 95.2%, 98.6%) on the test sets, respectively. This study illustrates the effective integration of supervised machine learning methods into diagnostic systems to produce early, accurate, interpretable diagnoses of disease. This study reinforces the proposed information technology approach for breast cancer prognosis. Limitations of the study are a moderately sized, homogeneous cohort, and restricted focus on structured variables, which may enhance internal validity while restricting generalizability. Our findings contribute to an emerging body of literature that well-tuned, regularized logistic regression provides a reasonable baseline against which breast cancer risk and other study outcomes can be compared, and a pragmatic route toward trustworthy AI in oncology.