Machine learning approaches for predicting breast cancer recurrence using clinical and histopathological data
Mohd Abas Bhat, Mushtaq Ahmad Mir, R. Vijaya Lakshmi, Tejaswini Pradhan, G. V. V. Jagannadha Rao, Ghanshyam G. Tejani, Syed Abid Hussain · Clinical and Experimental Medicine · 2025
Breast cancer remains the most common malignancy among women worldwide, with recurrence representing a major clinical challenge. Although significant progress has been made in early detection and treatment, recurrence affects up to 40% of patients in Brazil, influencing survival outcomes and therapeutic decisions. In this context, Machine Learning offers valuable potential for enhancing recurrence prediction by enabling data-driven risk assessment and personalized patient care. In the present study, clinical and histopathological information was extracted from unstructured medical records of breast cancer patients. A clustering technique (K-Means) was applied to identify patient subgroups with varying tumor aggressiveness profiles. Survival outcomes were further analyzed using the Cox proportional hazards model. Two distinct subgroups were identified: for less aggressive tumors, Quadratic Discriminant Analysis achieved a remarkably high recall of 0.9872, while for more aggressive tumors, Random Forest provided the most favorable trade-off between recall (0.7296) and precision (0.6811). Future research should explore validation across multiple institutions, incorporate molecular biomarkers, and leverage deep learning approaches to enhance predictive performance.