Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
The classification of human tremor signals using artificial neural network
Expert Systems with Applications: An International Journal
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
Prediction of Parkinson's disease tremor onset using radial basis function neural networks
Expert Systems with Applications: An International Journal
Nonlinear Models Using Dirichlet Process Mixtures
The Journal of Machine Learning Research
Multiclass relevance vector machines: sparsity and accuracy
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
Expert Systems with Applications: An International Journal
A comparison of regression methods for remote tracking of Parkinson's disease progression
Expert Systems with Applications: An International Journal
Parkinson's disease classification using gait characteristics and wavelet-based feature extraction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this paper, we present a novel approach for the identification of critical brain regions responsible for Parkinson's disease (PD) based on magnetic resonance images (MRI) using meta-cognitive radial basis function network (McRBFN) classifier with Recursive Feature Elimination (RFE). The McRBFN classifier uses voxel based morphometric (VBM) features extracted from MRI and employs a projection based learning (PBL) algorithm. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. Since, the classifier developed using PBL-McRBFN is efficient, we propose recursive feature elimination approach (called PBL-McRBFN-RFE) to identify most relevant brain regions responsible for PD prediction. The study has been conducted using the Parkinson's Progression Markers Initiative (PPMI) data set. First, we conducted the study on PD prediction using the PBL-McRBFN classifier on the PPMI data set. We have also compared the results of the PBL-McRBFN classifier with the support vector machine (SVM) classifier. The study results clearly show that the PBL-McRBFN classifier produces better generalization performance on PD prediction. Finally, we use RFE approach with PBL-McRBFN to identify the brain regions responsible for PD. The PBL-McRBFN-RFE selected features indicate that the loss of gray matter in the superior temporal gyrus region may be responsible for the onset of PD, and is consistent with the earlier findings from medical research studies.