A parallel neural network approach to prediction of Parkinson's Disease

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson's Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson's Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

[1]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[2]  Resul Das,et al.  A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..

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

[4]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[5]  Jinyan Fan,et al.  A note on the Levenberg-Marquardt parameter , 2009, Appl. Math. Comput..

[6]  Byoung Jik Lee.  Parallel neural networks for speech recognition , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[7]  Anna Esposito,et al.  Nonlinear Speech Modeling and Applications, Advanced Lectures and Revised Selected Papers, 9th International Summer School "Neural Nets E.R. Caianiello" on Nonlinear Speech Processing: Algorithms and Analysis, Vietri sul Mare, Salerno, Italy, September 13-18, 2004 , 2005, Summer School on Neural Networks.

[8]  Rasit Köker,et al.  Reliability-based approach to the inverse kinematics solution of robots using Elman's networks , 2005, Eng. Appl. Artif. Intell..

[9]  Mark J. F. Gales,et al.  Model-based techniques for noise robust speech recognition , 1995 .

[10]  Leandro Aureliano da Silva,et al.  Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction , 2008, Comput. Electr. Eng..

[11]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[12]  Francesco Piazza,et al.  Nonlinear Speech Enhancement: An Overview , 2005, WNSP.

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  Jacek M. Zurada,et al.  Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.

[15]  Louis D. Braida,et al.  Human and machine consonant recognition , 2005, Speech Commun..

[16]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[17]  John G. Harris,et al.  Applied principles of clear and Lombard speech for automated intelligibility enhancement in noisy environments , 2006, Speech Commun..

[18]  Qin Yan,et al.  Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement , 2008, Comput. Speech Lang..

[19]  Byoung-Tak Zhang,et al.  AESNB: Active Example Selection with Naïve Bayes Classifier for Learning from Imbalanced Biomedical Data , 2009, 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering.