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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.
Artificial Neural Network, Early Prediction, GABA, Parkinson’s Disease, Striatal Binding Ratio.
- Koutouzis TK, Stone D. Parkinson’s disease. Medicine Health Archives. Available from: http://www. emedicinehealth.com/parkinson_disease/article_em.htm
- Lee S-H. Identifying people with Parkinson’s Disease using foot pressure data. Indian Journal of Science and Technology. 2015 Jul; 8(13). Doi no:10.17485/ijst/2015/v8i13/73195
- Fahn S. Description of Parkinson’s Disease As A Clinical Syndrome. Ann NY: Acad Sci; 2003. p. 1-14.
- Jankovic J. Parkinson’s Disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery and Psychiatry. 2008; 79:368–76.
- Bairactaris C, Demakopoulos N, Tripsianis G, Sioka C, Farmakiotis D, Vadikolias K, Heliopoulos I, Georgoulias P, Tsougos I, Papanastasiou I, Piperidou C. Impact of dopamine transporter single photon emission computed tomography imaging using I-123 ioflupane on diagnoses of patients with Parkinsonian syndromes. Journal of Clinical Neuroscience. 2009; 16:246-52.
- Booij J, Tissingh G, Boer GJ, Speelman JD, Stoof JC, Janssen AG, Wolters EC, van Royen EA. [123I] FP-CIT SPECT shows a pronounced decline of striatal dopamine transporter labeling in early and advanced Parkinson's disease. Journal of Neurology, Neurosurgery and Psychiatry. 1997; 62:133-40.
- American College for Advancement in Medicine. Monograph, Alternative Medicine Review. 2007; 12:274-9.
- Kuroda H. Gamma-Amino Butyric Acid (GABA) in cerebrospinal fluid. Acta Med Okayama. 1983; 37(3):67-77.
- Abbott RJ, Pye IF, Nahorski SR. CSF and plasma GABA levels in Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry. 1982; 45:253-6.
- Koker R, Altinkok N, Demir A. Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms. Materials and Design. 2007; 28:616-27.
- Yaman N, Şenol FM, Gurkan P. Applying artificial neural networks to total hand evaluation of disposable diapers. Journal of Engineered Fibers and Fabrics. 2011; 6(1):38–43.
- Jiang Z, Zhang Z, Friedrich K. Prediction on wear properties of polymer composites with artificial neural networks. Composites Science and Technology. 2007 Feb; 67(2):168-76.
- Rao HS, Mukherjee A. Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites. Computational Materials Science. 1996; 5(4):307-22.
- Ozerdem MS, Kolukisa S. ANN approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys. Materials and Design. 2009; 30:764–69.
- Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience and Biobehavioral Reviews. 2012; 36:1140–52.
- Bind S, Tiwari AK, Sahani AK. A survey of machine learning based approaches for Parkinson Disease prediction. 2015; 6(2):1648-55.
- Winogrodzka A, Bergmans P, Booij J, van Royen EA, Janssen AG, Wolters EC. [123I] FP-CIT SPECT is a useful method to monitor the rate of dopaminergic degeneration in early-stage Parkinson’s disease. Journal of Neural Transmission. 2001; 108:1011–19.
- Rojas A, Gorriz JM, Ramirez J, Illan, Martnez-Murcia IA, Ortiz A, et al. Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease. Expert Systems with Applications. 2013; 40:2756–66.
- Available from: https://ida.loni.usc.edu/login.jsp?loginKey=8820611794027358579anduserEmailfirstname.lastname@example.org=PPMI
- Oliveira FPM, Castelo-Branco M. 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. J Neural Eng. 2015; 12(10). Doi:10.1088/1741-2560/12/2/026008.
- Seibyl J, Jennings D, Grachev I, Coffey C, Marek K. 123-I Ioflupane SPECT Measures of Parkinson Disease Progression in the Parkinson Progression Marker Initiative (PPMI) Trial. Society of Nuclear Medicine Annual Meeting Abstracts; 2013. p. 190.
- Jie XL, Davim JP, Cardoso R. Prediction oftri biological behavior of composite PEEK-CF30 using ANN. Journal of Materials Processing Technology. 2007; 189:374-8.
- Aleksendric D, Cirovic V. Smart brakes–neuro-genetic control of brake actuation pressure. In: Bacciga A, Nalito R, editors. Recent advances in Artificial Intelligence Research. NewYork: Nova Science Publishers; 2013. p. 85-102.
- Aleksendric D, Duboka C. Prediction of automotive friction material characteristics using artificial neural networks-cold performance. Wear. 2006; 261(3-4):269-82.
- El-Kadi H. Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using ANN: A review. Composite Structures. 2006; 73:1–23.
- Kartalopoulos SV. Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications. IEEE Press; 1996.
- Kumar Chandar S, Sumathi M, Sivanandam SN. Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian Journal of Science and Technology. 2016 Feb; 9(8). Doi no:10.17485/ijst/2016/v9i8/87905
- Schalkoff RJ. Artificial Neural Networks. McGraw-Hill; 1997.
- Rajasekaran S, Vijayalakshmi Pai GA. Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Application. New Delhi: Prentice-Hall of India Pvt., Ltd.; 2004.
- Astrom F, Koker R. A parallel neural network approach to prediction of Parkinson’s Disease. Expert Systems with Applications. 2011; 38:12470–74.
- Gnana SK, Deepa SN. Research article review on methods to fix number of hidden neurons in neural networks. Hindawi Publishing Corporation Mathematical Problems in Engineering. 2013; 425740:1- 11.
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