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@article{191693,
        author = {Roselinevinnarasi A and Hannah Inbarani H and Haripriya K.P},
        title = {Feature Reduction and SVM-Based Ensemble Machine Learning Techniques For Breast Cancer Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7418-7424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191693},
        abstract = {Breast Cancer is a leading health problem in women for the global community, and it is vital to screen at an early stage. To reduce the mortality rate of women, an automated diagnosis is required to identify the cancer. Machine Learning (ML) models were used to create the automated system. These models identify complex patterns in data and generate recommendations for health care professionals. In this work, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) models serve as base models for a soft-voting ensemble. Before execution, Principal Component Analysis (PCA) was conducted to diminish the dimensionality of the data and to enhance model performance. The proposed model was tested using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. A comparative analysis is performed between the proposed and state-of-the-art models for binary classification. The proposed model attains 96.49% as accuracy in diagnosing breast cancer. These results indicate the proposed model is useful and reliable for predictive breast cancer diagnosis.},
        keywords = {Machine Learning, Breast Cancer, SVM, KNN, and Ensemble (Soft Voting).},
        month = {January},
        }