Performance Analysis of Machine Learning Algorithms on Wisconsin Diagnostic Breast Cancer Data Set Enriched with Data Augmentation Technique
DOI:
https://doi.org/10.58190/ijamec.2026.171Keywords:
Breast cancer diagnosis, Classification, Data augmentation, Machine LearningAbstract
Breast cancer is a life-threatening cancer worldwide and is commonly seen in women. Early and accurate diagnosis is a key to successful treatment and better survival rates. In this study, using 569 samples from the Wisconsin Diagnostic Breast Cancer dataset, the classification performances of Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), Random Forest (RF), Naïve Bayes (NB) and k-Nearest Neighbor (kNN) algorithms are compared. To improve accuracy, the train dataset was expanded from 381 to 1524 samples using a three-stage method involving random scaling, Gaussian noise, and mathematical transformations. Test dataset remained unchanged. Statistical analyses confirmed that the augmented data presented sufficient variability to enhance generalization while preserving the original distribution. Model performance analysis was conducted using accuracy, precision, sensitivity, F1 score, FDR, MCC and AUC values. Compared to the original dataset, the accuracy of the SVM algorithm improved from 98.94% to 99.47% with the data augmentation techniques. This result shows that data augmentation techniques are effective in improving the classification performance of machine learning models for breast cancer diagnosis.
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