Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
IEEE Transactions on Neural Networks
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Most of the current advanced clinical decision support systems rely on some form of computational intelligence methodologies. As the machine intelligence paradigm shifted towards brain-inspired computing approach, it is interesting to investigate the performance of such a computing methodology in clinical data analysis. The human cerebellum constitutes a vital part of the brain system that possesses the capability to accurately model highly nonlinear physical dynamics. This paper presents a novel brain-inspired computational model of the human cerebellum named the kernel density-based CMAC(KCMAC) model for clinical decision support. The structure of the KCMAC model is inspired by the neurophysiological aspects of cerebellar learning and development process. The proposed KCMAC model is then applied to two medical case studies; namely, breast cancer diagnosis and the modeling of the human glucose metabolic process. The experimental results are encouraging.