Early Prediction of Parkinson's Disease using Artificial Neural Network

Objectives: The objective of this work is to present an in-depth understanding of the diagnosis of Parkinson’s Disease (PD) is critical for efficacious neuroprotection in the early stage. Diagnostic tools based on machine learning techniques using Striatal Binding Ratio (SBR) of Caudate and Putamen (left and right) are very useful to identify early PD. Methods: This paper presents an approach to develop an ANN model for prediction of Gamma-Amino Butyric Acid (GABA) concentration level for PD and Healthy Group (HG). Using multilayer perception network having 4-30-1 architecture for predicting GABA concentration level. The network is trained to an optimum level and trained network that predicts the GABA concentration level for the interpolated values of input parameters like Striatal Binding Ratio (SBR) of Caudate left, Caudate right and Putamen left, Putamen right. Findings: According to the ANN model, the prediction performance is highly encouraging with minimum error and high accuracy. The intended prediction model for GABA concentration level overcomes misdiagnosis of early PD. Applications: We propose to study the improvement of the early prediction of Parkinson’s disease, by implementing the ANN. The predictive model for diagnosing Parkinson disease using artificial neural network is efficient in early detection of neurogenerative disorders.

[1]  J. Davim,et al.  Prediction on tribological behaviour of composite PEEK-CF30 using artificial neural networks , 2007 .

[2]  Dragan Aleksendrić,et al.  Prediction of automotive friction material characteristics using artificial neural networks-cold performance , 2006 .

[3]  Abhijit Mukherjee,et al.  Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites , 1996 .

[4]  J. Jankovic.  Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[5]  Zhenyu Jiang,et al.  Prediction on wear properties of polymer composites with artificial neural networks , 2007 .

[6]  Sanguthevar Rajasekaran,et al.  Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications , 2003 .

[7]  J. Booij,et al.  [123I]FP-CIT SPECT is a useful method to monitor the rate of dopaminergic degeneration in early-stage Parkinson's disease , 2001, Journal of Neural Transmission.

[8]  I. Pye,et al.  CSF and plasma GABA levels in Parkinson's disease , 1982, Journal of neurology, neurosurgery, and psychiatry.

[9]  Arvind Kumar Tiwari,et al.  A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction , 2015 .

[10]  Raşit Köker,et al.  Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms , 2007 .

[11]  I. Tsougos,et al.  Impact of dopamine transporter single photon emission computed tomography imaging using I-123 ioflupane on diagnoses of patients with parkinsonian syndromes , 2009, Journal of Clinical Neuroscience.

[12]  Freddie Åström,et al.  A parallel neural network approach to prediction of Parkinson's Disease , 2011, Expert Syst. Appl..

[13]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[14]  M. S. Ozerdem,et al.  Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys , 2009 .

[15]  J D Speelman,et al.  [123I]FP-CIT SPECT shows a pronounced decline of striatal dopamine transporter labelling in early and advanced Parkinson's disease. , 1997, Journal of neurology, neurosurgery, and psychiatry.

[16]  Juan Manuel Górriz,et al.  Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease , 2013, Expert Syst. Appl..

[17]  Sang-Hong Lee.  Identifying People with Parkinson's Disease using Foot Pressure Data , 2015 .

[18]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[19]  Necla Yaman,et al.  Applying Artificial Neural Networks to Total Hand Evaluation of Disposable Diapers , 2011 .

[20]  S. Fahn.  Description of Parkinson's Disease as a Clinical Syndrome , 2003, Annals of the New York Academy of Sciences.

[21]  H. Kuroda.  Gamma-aminobutyric acid (GABA) in cerebrospinal fluid. , 1983, Acta medica Okayama.

[22]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

[23]  Miguel Castelo-Branco,et al.  Computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines , 2015, Journal of neural engineering.

[24]  S. N. Sivanandam,et al.  Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network , 2016 .

[25]  John Seibyl,et al.  123-I Ioflupane SPECT measures of Parkinson disease progression in the Parkinson Progression Marker Initiative (PPMI) trial , 2013 .

[26]  Robert J. Schalkoff,et al.  Artificial neural networks , 1997 .

[27]  Hany El Kadi,et al.  Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—A review , 2006 .

[28]  E. Wolters,et al.  FP-CIT SPECT is a useful method to monitor the rate of dopaminergic degeneration in early-stage Parkinson ’ s disease , 2001 .