Breast cancer remains one of the most prevalent diseases that affect women worldwide. Making an early and accurate diagnosis is essential for effective treatment. Machine learning (ML) techniques have increasingly been utilized in biomedical informatics to enhance diagnostic accuracy and efficiency. This study proposes a vector quantization (VQ) model as a robust approach for clustering high-dimensional medical data, particularly in breast cancer classification. The model evolves over time to better match the input data distribution. This adaptive feature is a strength of the model, as it allows the cluster centers to shift according to the input patterns, effectively quantizing data distribution. It is a gradient dynamical system, using the energy function V as its Lyapunov function, and thus possesses properties of convergence and stability. In this study, we have applied the dynamic model to the “Breast Cancer Wisconsin Diagnostic” dataset, a comprehensive collection of features derived from digitized images of fine needle aspirate (FNA) of breast masses. This dataset comprises various diagnostic measurements related to breast cancer and poses a unique challenge for clustering due to its high dimensionality and the critical nature of its application in medical diagnostics. Using the model, we aim to demonstrate its efficacy in handling complex multidimensional data, especially in the realm of medical pattern recognition and data mining. This integration not only highlights the model’s versatility in different domains but also showcases its potential to contribute significantly to medical diagnostics, particularly in breast cancer identification and classification.
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