Diagnosing Parkinson by using artificial neural networks and support vector machines

Parkinson\'s Disease (PD) is the second most common neurodegenerative ai?½iction only surpassed by Alzheimer\'s Disease (AD). Moreover, it is expected to increase in the next decade with accelerating treatment costs as a consequence. This situation leads us towards the need to develop a Decision Support System for PD. In this paper we propose methods based on ANNs and SVMs to aid the specialist in the diagnosis of PD. Data recorded during 195 examinations carried out on 31 patients was used to verify the capacity of the proposed system. The results show a high accuracy of around 90%.

[1]  Marcello Imbriani,et al.  Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals. , 2009, European journal of medicinal chemistry.

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  E. Tolosa,et al.  The diagnosis of Parkinson's disease , 2006, The Lancet Neurology.

[4]  Hua Li,et al.  Dimensionality reduction for knowledge discovery in medical claims database: Application to antidepressant medication utilization study , 2009, Comput. Methods Programs Biomed..

[5]  Christian Balkenius,et al.  A model of attentional impairments in autism: first steps toward a computational theory , 2005, Cognitive Systems Research.

[6]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[7]  Omolola Ogunyemi,et al.  A comparison of methods for assessing penetrating trauma on retrospective multi-center data , 2009, J. Biomed. Informatics.

[8]  Chitta Baral,et al.  Fuzzy C-means Clustering with Prior Biological Knowledge , 2022 .

[9]  P. Brown,et al.  Online prediction of self-paced hand-movements from subthalamic activity using neural networks in Parkinson’s disease , 2004, Journal of Neuroscience Methods.

[10]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[11]  L. Buydens,et al.  Visualisation and interpretation of Support Vector Regression models. , 2007, Analytica chimica acta.

[12]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[13]  Paola Piccini,et al.  Functional brain imaging in the differential diagnosis of Parkinson's disease , 2004, The Lancet Neurology.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Max A. Little,et al.  Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007, Biomedical engineering online.

[17]  Ira L. Cohen,et al.  An artificial neural network analogue of learning in autism , 1994, Biological Psychiatry.

[18]  J. Jankovic,et al.  The evolution of diagnosis in early Parkinson disease. Parkinson Study Group. , 2000, Archives of neurology.

[19]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[20]  Ira L. Cohen,et al.  Neural Networks and Psychopathology: Neural network analysis of learning in autism , 1998 .

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  David L. Cardozo,et al.  THE CANADIAN JOURNAL OF NEUROLOGICAL SCIENCES , 2002 .

[23]  J. Hughes,et al.  Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[24]  M. Pal.  Factors influencing the accuracy of remote sensing classifications : a comparative study , 2002 .

[25]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[26]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[27]  Dr. S. N. Panda,et al.  Global Journal of Computer Science and Technology , 2010 .

[28]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[29]  Daniel J Schaid,et al.  Risk tables for parkinsonism and Parkinson's disease. , 2002, Journal of clinical epidemiology.

[30]  C. Goose,et al.  Glossary of Terms , 2004, Machine Learning.

[31]  Shalini Jha,et al.  The diagnosis of Parkinson's disease. , 2003, Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology.

[32]  R. A. Naumov,et al.  Recognition of ongoing mental activity with artificial neural network , 2008 .

[33]  L. Buydens,et al.  Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel , 2006 .

[34]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[35]  A. Rajput,et al.  Accuracy of Clinical Diagnosis in Parkinsonism — A Prospective Study , 1991, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.