Abstract
A multiple order model migration (MOMM) algorithm and optimal model selection strategy are proposed here for rapid model development and online glucose prediction. First, the optimal model order is determined for each input and a multiple order prediction model is used. Then a MOMM algorithm is developed based on particle swarm optimization to simultaneously revise multiple parameters. The multiple order parameters of each input in the old model are quickly customized so that the revised model can be used for new subjects with desirable prediction accuracy. In particular, the influences of the amount of modelling samples are analyzed to check the applicability of different methods in order to suggest the selection guideline of optimal model in response to different data sizes. The above issues are investigated for two types of analysis and thirty in silico subjects. For same-case analysis, three regions are considered. In Region I, first order model migration (FOMM) achieves the best performance. In Region II, MOMM algorithm should be used and the prediction accuracy is superior. With enough samples (Region III), subject-dependent model (SM) algorithm can be chosen. In contrast, for cross-case analysis, MOMM can reveal more powerful generalization ability than SM, so that only two regions are considered. FOMM achieves the best performance in Region I and MOMM algorithm is superior in Region II when the number of samples is larger than 4h. The MOMM algorithm is demonstrated to be able to transfer model for new subjects with more reasonable model structure. Besides, each algorithm has its applicability regarding the size of modelling samples.
Published Version
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