Breast cancer recurrence prediction with deep neural network and feature optimization

Abstract

Breast cancer remains a pervasive global health concern, necessitating continuous efforts to attain effectiveness of recurrence prediction schemes. This work focuses on breast cancer recurrence prediction using two advanced architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), integrated with feature selection techniques utilizing Logistic Regression (LR) and Analysis of Variance (ANOVA). The well-known Wisconsin cancer registry dataset, which contains vital diagnostic data from breast mass fine-needle aspiration biopsies, was employed in this study. The mean values of accuracy, precision, recall and F1-score for the proposed LR-CNN-LSTM model were calculated as 98.24%, 99.14%, 98.30% and 98.14% respectively. The mean values of accuracy, precision, recall and F1-score for the proposed ANOVA-GRU model were calculated as 96.49%, 97.04%, 96.67% and 96.67% respectively. The comparison with traditional methods showcases the superiority of our proposed approach. Moreover, the insights gained from feature selection contribute to a deeper understanding of the critical factors influencing breast cancer recurrence. The combination of LSTM and GRU models with feature selection methods not only enhances prediction accuracy but also provides valuable insights for medical practitioners. This research holds the potential to aid in early diagnosis and personalized treatment strategies.

Keywords

• Breast cancer
• recurrence
• prediction
• deep learning
• LSTM
• GRU