Predicting Breast Cancer Malignancy from Diagnostic Imaging Features
An Empirical Comparison of Machine Learning Models for Healthcare Analytics
Keywords:
healthcare analytics machine learning breast cancer diagnosis, logistic regression random forest, clinical decision support , explainable AI, algorithmic biasAbstract
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)....
Downloads
Global Statistics ℹ️
|
0
Views
|
0
Downloads
|
|
0
Total
|
|
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Authors retain copyright and transfer to the journal the right of first publication and publishing rights
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Those authors who publish in this journal accept the following terms:
-
Authors retain copyright.
-
Authors transfer to the journal the right of first publication. The journal also owns the publishing rights.
-
All published contents are governed by an Attribution-NoDerivatives 4.0 International License.
Access the informative version and legal text of the license. By virtue of this, third parties are allowed to use what is published as long as they mention the authorship of the work and the first publication in this journal. If you transform the material, you may not distribute the modified work. -
Authors may make other independent and additional contractual arrangements for non-exclusive distribution of the version of the article published in this journal (e.g., inclusion in an institutional repository or publication in a book) as long as they clearly indicate that the work was first published in this journal.
- Authors are allowed and recommended to publish their work on the Internet (for example on institutional and personal websites), following the publication of, and referencing the journal, as this could lead to constructive exchanges and a more extensive and quick circulation of published works (see The Effect of Open Access).
