ArticleProceedings. International Conference on Advanced Information Networking and Applications2024
Comparative Analysis of Data Preprocessing Methods in Machine Learning for Breast Cancer Classification.
Article in Proceedings. International Conference on Advanced Information Networking and Applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Breast cancer, the most diagnosed cancer according to the American Cancer Society, impacts about 13% of women and has a rising incidence rate. With a 10-year survival rate of 84%, early detection is essential, especially in cases of dense breast tissue where 41% go undetected. This paper explores various feature selection and extraction techniques in machine learning for breast cancer classification, including correlation-based selection, recursive elimination, linear discriminant analysis, principal component analysis, and their combinations. The experimental evaluation with the well-known Wisconsin Diagnostic Breast Cancer dataset shows that the linear discriminant analysis feature transformation provides the best overall performance for machine learning algorithmic classifications regarding Accuracy, Precision, Recall, and F1-Score.
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