A Statistically Validated and Interpretable Machine Learning Framework for Breast Cancer Classification Using Shape Descriptors

Authors

  • K. A. Sotonwa Department of Computer Science, Lagos State University, Nigeria
  • J. O. Akintayo Department of Computer Science, Lagos State University, Nigeria
  • A. Pearson Department of Medicine, University of Chicago, Chicago, USA
  • A. Sofoluwe Department of Radiology, University of Ibadan College of Medicine, Ibadan, Nigeria
  • S. Arekete Department of Computer Science, Redeemer’s University, Ede, Nigeria
  • S. Sammet Center for Clinical Cancer Genetics and Global Health, University of Chicago; Chicago, USA
  • F. Olopade Department of Medicine, University of Chicago, Chicago, USA & Center for Clinical Cancer Genetics and Global Health, University of Chicago; Chicago, USA
  • B. Aribisala Department of Computer Science, Redeemer’s University, Ede, Nigeria & Department of Medicine, University of Chicago, Chicago, USA

DOI:

https://doi.org/10.63746/njtd.v23i1.4248

Keywords:

Breast Cancer, Feature selection, Machine Learning Algorithm, Shape Descriptor, Chi square

Abstract

Breast cancer (BC) remains a major global health priority, with nearly half a million invasive cases reported in 2024. Early diagnosis is critical, and Machine Learning (ML) offers significant potential for automated detection. This study evaluated ML models using tumour shape descriptors from the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (569 cases: 357 benign, 212 malignant). Ten numerical descriptors, including radius, texture, and concave points, were extracted from digitized fine needle aspirates (FNA). The dataset was split 70:30 into training and test sets, with Chi-square feature selection, mutual information (MI), and ANOVA feature selection applied to reduce dimensionally and prevent data leakage. Three models: Artificial Neural Network (ANN), Classification and Regression Tree (CART), and Logistic Regression (LR) were trained using stratified cross-validation and evaluated on the independent test set. LR achieved the highest AUC (97.31%), significantly outperforming ANN (p = 0.009) and CART (p = 0.001) by DeLong’s test, while McNemar’s test revealed no significant differences in misclassification rates (p > 0.05). CART achieved the best F1-score, reflecting balanced sensitivity and specificity. These findings demonstrated that statistically validated and interpretable ML models provide robust diagnostic performance for BC classification

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Published

2026-03-31

Issue

Vol. 23 No. 1 (2026): Volume 23, Issue 1, March 2026.

Section

Articles

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