Title: Radial basis function network equipped with an ensemble-based Lasso ridge model in diagnosis of breast cancer

Authors: Pooja J. Shah; Trupti P. Shah

Addresses: Department of Applied Mathematics, The Maharaja Sayajirao University of Baroda, Vadodara – 390001, Gujarat, India ' Department of Applied Mathematics, The Maharaja Sayajirao University of Baroda, Vadodara – 390001, Gujarat, India

Abstract: The information that is kept in the form of medical records is of tremendous assistance when it comes to the process of putting together medical decision support systems. The goal of this study is to provide a description of two distinct methods for the development of a medical diagnostic tool that is driven by data in the detection of breast cancer. The radial basis function network (RBFN) architecture with Lasso and ridge regularisation, as well as ensemble learning, are both methods that have been proposed as potential solutions. Following the implementation of the proposed networks on the Wisconsin Breast Cancer (WBC) dataset, comparative analysis is carried out.

Keywords: radial basis function network; RBFN; Lasso and ridge regularisation; LR; ensemble learning; breast cancer; BC.

DOI: 10.1504/IJMEI.2025.148643

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.5, pp.487 - 499

Received: 18 Jun 2022
Accepted: 10 Feb 2023
Published online: 17 Sep 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article