Title: Risk estimation of breast cancer patient with METABRIC clinical data: an elucidative study of machine learning algorithms with time sensitive information

Authors: Rajan Prasad Tripathi; Sunil Kumar Khatri; Darelle Van Greunen; Danish Ather

Addresses: Department of IT and Engineering, Amity University in Tashkent, Tashkent, Uzbekistan ' Research, Innovation and Extension Activities, Amity University, Noida, India ' Department of Electrical Engineering, Centre for Community Technologies, Nelson Mandela University, Port Elizabeth, South Africa ' Department of IT and Engineering, Amity University in Tashkent, Tashkent, Uzbekistan

Abstract: Breast cancer is a prevalent and life-altering disease that demands precise prognostic tools to guide treatment decisions. Machine learning (ML), with its data-driven capabilities, has emerged as a promising avenue for improving breast cancer prognosis. In this study, we harness the power of machine learning to predict breast cancer survival using clinical data sourced from the METABRIC dataset. Our research sheds light on the critical clinical factors that intimately influence patient outcomes. Among seven distinct algorithms evaluated, Logistic Regression stands out with the highest accuracy of 78%. Notably, our findings underscore the pivotal role of time-related data in enhancing predictive performance, advocating for its inclusion in future prognostic models. We identify positive correlations between survival and parameters such as tumour size and breast-conserving surgery, where the latter exhibits a correlation coefficient of 0.18. Conversely, a negative correlation emerges with breast mastectomy surgery, with a correlation coefficient of –0.18. This study not only points to robust machine learning models for prognosis but also highlights the intricate interplay between time-sensitive information and breast cancer prognosis. By doing so, it deepens our understanding of breast cancer prognosis and potentially informs more effective treatment strategies.

Keywords: breast cancer; METABRIC; machine learning; patient survival; risk estimation.

DOI: 10.1504/IJCVR.2026.155193

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.1, pp.78 - 98

Received: 15 Sep 2023
Accepted: 15 Nov 2023
Published online: 29 Jul 2026 *

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