APPLICATION-ORIENTED MACHINE LEARNING APPROACH FOR BREAST CANCER DIAGNOSIS USING CLINICAL FEATURE ANALYSIS
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Abstract
Breast cancer remains a major global health challenge, where early and accurate diagnosis is essential for improving patient survival rates and treatment outcomes. This study proposes an application-oriented machine learning approach for breast cancer diagnosis using clinical feature analysis. A structured dataset containing multiple patient records and quantitative features derived from fine needle aspirate (FNA) images was utilized to develop predictive models. Several machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbours (KNN), and Decision Tree, were implemented and evaluated. Data preprocessing techniques such as normalization, feature selection, and train-test splitting were applied to enhance model performance. The results demonstrated that the Random Forest model achieved the highest accuracy of 98%, outperforming other classifiers in terms of precision, recall, and F1-score. Confusion matrix analysis indicated minimal misclassification, while ROC-AUC evaluation confirmed strong discriminative capability, with an AUC score of 0.99. Feature importance analysis revealed that clinical attributes such as concave points, perimeter, and radius significantly contribute to diagnosis. Despite moderate class imbalance, the models exhibited robust generalization performance. Overall, the study highlights the effectiveness of integrating machine learning with clinical feature analysis to develop accurate, interpretable, and reliable diagnostic systems for early breast cancer detection, supporting improved clinical decision-making and patient care.
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