Comparison of Machine Learning Models for Detecting Breast Cancer Using WEKA Software

Breast cancer is a major health concern, and early diagnosis is crucial for treatment. In this paper we present a machine learning-based approach for classifying breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. CfsSubsetEval with GreedyStepwise is used to select the most relevant features and reduce redundancy. The selected features are evaluated using Logistic Regression, Multilayer Perceptron (MLP) classifiers and Sequential Minimal Optimization (SMO). The models are compared using accuracy, precision, sensitivity, specificity, F1-score, and ROC-AUC. The goal of this study is to identify an efficient classifier and a compact feature subset for reliable breast cancer classification.