کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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384664 | 660853 | 2013 | 11 صفحه PDF | دانلود رایگان |
In this paper, we propose a gene expression based approach for the prediction of Parkinson’s disease (PD) using ‘projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)’. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, a cognitive component and a meta-cognitive component. The cognitive component is a radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn of human learning principles efficiently.PBL-McRBFN classifier is used to predict PD using micro-array gene expression data obtained from ParkDB database. The performance of PBL-McRBFN classifier has been evaluated using Independent Component Analysis (ICA) reduced features sets from the complete genes and selected genes with two different significance levels. Further, the performance of PBL-McRBFN classifier is statistically compared with existing classifiers using one-way repeated ANOVA test. Further, it is also used in PD prediction using the standard vocal and gait PD data sets. In all these data sets, the performance of PBL-McRBFN is compared against existing results in the literature. Performance results clearly highlight the superior performance of our proposed approach.
► Gene Expression profiles are used for Parkinson disease detection using PBL-McRBF classifier.
► PBL-McRBF evolves the network architecture automatically.
► Meta-cognitive learning to emulate human learning components.
► Sample learning/deletion/reserve strategies helps in capturing knowledge efficiently.
► Gait and Vocal features are also used for Parkinson disease detection.
Journal: Expert Systems with Applications - Volume 40, Issue 5, April 2013, Pages 1519–1529