Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms for parameter estimation in mathematical modeling of glucose metabolism
Computers in Biology and Medicine
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Neural-network models for the blood glucose metabolism of a diabetic
IEEE Transactions on Neural Networks
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
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The evaluation of insulin sensitivity plays an important role in the clinical investigation of glucose related diseases. Mathematical models based on non-invasive tests provide an estimate of insulin sensitivity by solving a nonlinear optimization problem. However traditional optimization methods suffer from convergence problem and the final estimate is heavily dependent on the initial parameterization. This paper proposes a model based on the hybrid approach of nonlinear autoregressive with exogenous input (NARX) modeling and genetic algorithm (GA) for deriving an index of insulin sensitivity. The model does not need an initial parameterization and the convergence is always guaranteed. The index derived from the proposed model is found to correlate well with the widely used minimal model based insulin sensitivity, with a significantly higher accuracy of fit.