scholarly journals Feature Selection and Radial Basis Function Network for Parkinson Disease Classification

2017 ◽  
Vol 2 (3) ◽  
pp. 167-171
Author(s):  
Ashraf Osman Ibrahim ◽  
Walaa Akif Hussien ◽  
Ayat Mohammoud Yagoop ◽  
Mohd Arfian Ismail
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Parkinson Disease ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Feature Selection Method ◽  
Selection Method ◽  
Radial Basis

Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Parkinson’s disease. The feature selection (FS) method used to reduce the number of attributes in Parkinson disease data. The Parkinson disease dataset is acquired from UCI repository of large well-known data sets. The experimental results have revealed significant improvement to detect Parkinson’s disease using feature selection method and RBF network.

PREDICTION OF PARKINSON'S DISEASE TREMOR ONSET USING A RADIAL BASIS FUNCTION NEURAL NETWORK BASED ON PARTICLE SWARM OPTIMIZATION

2010 ◽  
Vol 20 (02) ◽  
pp. 109-116 ◽  
Author(s):  
DEFENG WU ◽  
KEVIN WARWICK ◽  
ZI MA ◽  
MARK N. GASSON ◽  
JONATHAN G. BURGESS ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis ◽  
Deep Brain

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

2010 ◽  
Vol 37 (4) ◽  
pp. 2923-2928 ◽  
Author(s):  
Defeng Wu ◽  
Kevin Warwick ◽  
Zi Ma ◽  
Jonathan G. Burgess ◽  
Song Pan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis

Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 32-49 ◽  
Author(s):  
Sanjeev Kumar Dash ◽  
Aditya Prakash Dash ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Information Gain ◽  
Radial Basis Function Network ◽  
Network Efficiency ◽  
Novel Approach ◽  
Radial Basis ◽  
Unbalanced Dataset

This work presents a novel approach for classification of both balanced and unbalanced dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact it size. In this paper, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

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