Comparative Evaluation of Supervised Learning Models for Breast Cancer Diagnosis

Anil Kumar R J, Monica R, Veena M N, Nirmala M S

Abstract: Finding breast cancer early and accurately is very important for helping patients live longer. We use the Wisconsin Diagnostic Breast Cancer (WDBC) dataset to look at two popular machine learning classifiers: Support Vector Machines (SVM) and k-nearest neighbors (KNN). Our work combines theory with practice by including things like data preprocessing exploratory feature evaluation, model configuration, hyperparameter, and validation through cross-validation techniques. We use common evaluation metrics like accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) to judge performance. When we did experiments with both a 70:30 train-test split and 10-fold cross-validation, we found that SVM did a little better (~96.3% accuracy) than compared to KNN, which has an accuracy of about 96.5% and AUC values of about 0.96 and 0.968, respectively. SVM is better at dealing with high-dimensional data and making maximum-margin decision boundaries, but KNN is still appealing because it is simple and works well on datasets of moderate size. Even though both methods have their pros and cons, they also have some problems that are talked about, as well as ways they could be improved and how they could be used in the clinic. This paper evaluates and compares the theoretical and practical performance of SVM and KNN using the WDBC dataset. We include preprocessing, careful evaluation metrics, and hyperparameter optimization to provide an empirical comparison of both models. The study also discusses important trade-offs and real-world factors for using these algorithms in medical screening contexts.

Keywords: Breast cancer detection, Machine learning classifiers, Support vector machine, k-nearest neighbors, medical screening

How to Cite?: Anil Kumar R J, Monica R, Veena M N, Nirmala M S, "Comparative Evaluation of Supervised Learning Models for Breast Cancer Diagnosis", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 856-863, https://www.ijsr.net/getabstract.php?paperid=SR251108143619, DOI: https://dx.doi.org/10.21275/SR251108143619