Evolutionary Algorithm Pipeline for Improving in Prediction of ‎Earlier Diagnosis of Breast Cancer

  • Authors

    • G. Sridevi Research Scholar, Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced ‎Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India
    • K. Sharmila Research Supervisor and Associate Professor in the Department of Computer Science, School of Computing Sciences, Vels Institute of Science, ‎Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India
    https://doi.org/10.14419/qs9zrt46

    Received date: July 15, 2025

    Accepted date: July 24, 2025

    Published date: November 1, 2025

  • Breast Cancer; Artificial Neural Network; Ensemble Learning; Machine Learning; Evolutionary Algorithm

  • Abstract

    The cells in the breast have been developed in terms of cancer named Breast Cancer (BC), and over the past decade, BC has been the most ‎identified type of cancer in women. Currently, the rapid increase in the predominance of BC may lead to increased mortality worldwide. The ‎disorder is diagnosed manually, which requires efficient expertise and time, both of which are significant in detecting cancer earlier and ‎informing subsequent treatments. There are various Machine Learning (ML) methods that assist in making several decisions as well as ‎performing diagnoses from the collected data, but have failed in accomplishing high prediction in diagnosing BC. This paper focuses on ‎investigating the probability of Ensemble Learning (EL) with Evolutionary Algorithm (EA) for risk prediction and BC categorization, ‎precisely through exact feature selection and extraction. Moreover, the key focus is on creating a consistent and precise model by associating ‎the best features of various learning algorithms and ensembling the metadata sets. Therefore, this research focuses on analyzing methods to ‎improve the accuracy of BC diagnosis through a proposed ensemble Artificial Neural Network (ANN) method with a Keras-based ‎approach. Hence, the evaluated results propose a method determined through accuracy metrics, which are compared with the Boruta method ‎and the ANN method. The accuracy of the Keras-based ensemble ANN method, at 97.37%, is higher than that of other ML methods‎.

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  • How to Cite

    Sridevi, G. ., & Sharmila , K. (2025). Evolutionary Algorithm Pipeline for Improving in Prediction of ‎Earlier Diagnosis of Breast Cancer. International Journal of Basic and Applied Sciences, 14(SI-1), 624-631. https://doi.org/10.14419/qs9zrt46