Title: Enhancing breast cancer risk prediction through comprehensive ensemble machine learning analysis: a study on clinical, genetic, and demographic factors

Authors: Chandrakant Mallick; Chitta Ranjan Behera; Subrat Kumar Parida; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Information Technology, Gandhi Institute of Technological Advancement (GITA) Autonomous College, Odisha, India ' Department of Computer Science and Engineering, Centurion University of Technology and Management, Odisha, India ' Department of Computer Science and Engineering, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India

Abstract: Breast cancer is a major worldwide health problem, and early risk assessment plays a crucial role in improving patient outcomes. In this study, we employ supervised machine learning techniques to comprehensively analyse and predict breast cancer risk. Leveraging a diverse dataset comprising clinical, genetic, and demographic factors, explore the predictive power of machine learning algorithms. Our comprehensive analysis delves into feature selection, model evaluation, and performance optimisation. The proposed ensemble model has been validated on Wisconsin Breast Cancer Diagnostic medical dataset. The importance of this research in the context of improved patient care, screening programs, and risk assessment tools. It contributes to the ongoing effort to enhance breast cancer risk prediction through advanced data-driven methods, paving the way for more effective preventive strategies and early interventions. It show that effective data pre-processing performed to the raw data and feature selection the model resulted in an enhanced accuracy of 98.24%.

Keywords: breast cancer; risk assessment; machine learning; predictive modelling; feature selection; early detection; personalised healthcare.

DOI: 10.1504/IJIMS.2025.148601

International Journal of Internet Manufacturing and Services, 2025 Vol.11 No.3, pp.191 - 209

Received: 27 Feb 2024
Accepted: 07 Apr 2024
Published online: 15 Sep 2025 *

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