Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Introduction to Data Mining Using SAS Enterprise Miner
Introduction to Data Mining Using SAS Enterprise Miner
Low-frequency vocal modulations in vowels produced by Parkinsonian subjects
Speech Communication
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
Monitoring neurological disease in phonation
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Neurological disease detection and monitoring from voice production
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
A comparison of regression methods for remote tracking of Parkinson's disease progression
Expert Systems with Applications: An International Journal
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evaluating data mining algorithms using molecular dynamics trajectories
International Journal of Data Mining and Bioinformatics
Expert Systems with Applications: An International Journal
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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In this paper, different types of classification methods are compared for effective diagnosis of Parkinson's diseases. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approaches described in this paper purpose to efficiently distinguish healthy individuals. Four independent classification schemas were applied and a comparative study was carried out. These are Neural Networks, DMneural, Regression and Decision Tree respectively. Various evaluation methods were employed for calculating the performance score of the classifiers. According to the application scores, neural networks classifier yields the best results. The overall classification score for neural network is 92.9%. Moreover, we compared our results with the result that was obtained by kernel support vector machines [Singh, N., Pillay, V., & Choonara, Y. E. (2007). Advances in the treatment of Parkinson's disease. Progress in Neurobiology, 81, 29-44]. To the best of our knowledge, our correct classification score is the highest so far.