Parkinsons Disease Classification using Neural Network and Feature Selection
In this study, the Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm are used to classify to effective diagnosis Parkinsons disease(PD).It-s a challenging problem for medical community.Typically characterized by tremor, PD occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. A feature selection algorithm along with biomedical test values to diagnose Parkinson disease.Clinical diagnosis is done mostly by doctor-s expertise and experience.But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis.In many cases,not all the tests contribute towards effective diagnosis of a disease.Our work is to classify the presence of Parkinson disease with reduced number of attributes.Original,22 attributes are involved in classify.We use Information Gain to determine the attributes which reduced the number of attributes which is need to be taken from patients.The Artificial neural networks is used to classify the diagnosis of patients.Twenty-Two attributes are reduced to sixteen attributes.The accuracy is in training data set is 82.051% and in the validation data set is 83.333%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071075Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2887
 M.F. Akay. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems With Applications, 2008.
 AJ Hughes, SE Daniel, L. Kilford, and AJ Lees. Accuracy of clinical diagnosis of idiopathic Parkinson-s disease: a clinico-pathological study of 100 cases. British Medical Journal, 55(3) 181184, 1992.
 M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, and L.O. Ramig. Suit- ability of dysphonia measurements for telemonitoring of Parkinson? s disease. IEEE transactions on bio-medical engineering, 2008.
 E. Tolosa, G. Wenning, and W. Poewe. The diagnosis of Parkinson-s disease. Lancet Neurology, 5(1):7586, 2006.
 GA Ivanitsky and RA Naumov. Recognition of ongoing mental activity with artificial neural network. International Journal of Psychophysiology, 69(3): 180180, 2008.
 Data Mining Techniques For Marketing,Sales, and Customer Support. John Wiley & Sons,Inc.
 A. Asuncion and D.J. Newman. UCI machine learning repository, 2007. URL http://www.ics.uci.edu/Ôê╝mlearn/MLRepository.html.
 Berry, M.J.A. and Linoff, G. (1999): Data Mining Techniques: For Marketing, Sales, and Customer Support. Morgan Kaufmann Publishers.