Predicting Breast Cancer Malignancy from Diagnostic Imaging Features

Abstract

Early and accurate diagnosis of breast cancer has a direct bearing on patient survival, yet interpretation of fine needle aspirate (FNA) imaging features still depends heavily on clinician experience and remains subject to inter-observer disagreement. This paper presents an empirical case study evaluating two supervised machine learning models — logistic regression and random forest — for classifying breast masses as malignant or benign using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (n = 569, 30 quantitative morphological features). Both models were trained on a stratified 75/25 train-test split and validated with 5-fold stratified cross-validation. Logistic regression produced the stronger held-out result (accuracy = 0.986, ROC-AUC = 0.998), edging out random forest (accuracy = 0.958, ROC-AUC = 0.995); cross-validation confirmed that both models generalized consistently across folds (mean ROC-AUC of 0.995 and 0.989, respectively)....