Parkinson’s disease prediction using gene expression – A projection based learning meta-cognitive neural classifier approach

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. RBFN 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.