Predicting breast cancer recurrence using deep learning

• Published in: Discover applied sciences Vol. 7; no. 2; pp. 113 - 33
• Main Authors: Kumari, Deepa, Naidu, Mutyala Venkata Sai Subhash, Panda, Subhrakanta, Christopher, Jabez
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 30.01.2025; Springer Nature B.V; Springer
• Subjects: Accuracy; Applied and Technical Physics; Breast cancer; Breast cancer recurrence; Cancer therapies; Chemistry/Food Science; Customization; Datasets; Decision trees; Deep learning; Disease management; Earth Sciences; Eigenvalues; Eigenvectors; Engineering; Environment; Feature engineering; Feature selection; Hybrid model; Hybridization; Machine learning; Mammography; Materials Science; Medical research; Multilayer perceptrons; Neural networks; Patients; Predictions; Regression analysis; Standard deviation; Standardization; Support vector machines; Visual discrimination learning; United States > US; Wisconsin; Deep learning; Hybrid model; Breast cancer recurrence; Feature engineering; Machine learning
• DOI: 10.1007/s42452-025-06512-5
• ISSN: 3004-9261; 2523-3963

Breast cancer and its recurrence are significant health concerns, emphasizing the critical importance of early detection and personalized treatment strategies for improved outcomes. This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel approach designed to predict breast cancer recurrence with high accuracy and interpretability. Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). This hybridization leads to the creation of 16 robust models that enhance interpretability, facilitate generalization, and effectively manage challenges related to small datasets, class imbalance, and data preprocessing. The BCR-HDL framework’s unique contributions include its ability to predict not only diagnostic outcomes but also prognostic and recurrence timing, offering a comprehensive solution for breast cancer management. Specifically, the Hybrid MLP+RF and Xception+RF models achieved an exceptional diagnostic accuracy of 97% on the WDBC dataset, while the Hybrid MLP+RF model reached 78% prognostic accuracy on the WPBC dataset. Moreover, the Hybrid ResNet+SVM and ResNet+RF models demonstrated impressive performance in multi-classifying recurrence into different time intervals, achieving 92% accuracy in predicting recurrence within 2 years, between 2 to 4 years, and beyond 4 years. The study also provides a detailed analysis of model performance through training versus validation accuracy graphs and a comparison with existing approaches, demonstrating the superiority of the proposed framework in terms of diagnostic, prognostic, and recurrence time predictions. The BCR-HDL framework offers practical recommendations for clinicians, including its potential for personalized treatment strategies and improved patient monitoring, making it a valuable tool for advancing breast cancer management. Highlights The BCR-HDL framework predicts breast cancer recurrence with high accuracy, enhancing early detection. It combines deep learning and traditional models to improve prediction of diagnostic, prognostic, and recurrence outcomes. Clinicians can use these insights to create personalized treatment plans and better monitor patient health.