A comparison of multiple classification methods for diagnosis of Parkinson disease

By Resul Das. Expert systems with applications, 2010

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Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease (2012, methodology): “…with the response, and exhibit statistically significant (p 0.001) correlation, but the more recently proposed VFER measures, and MFCCs, are more GP-EM, 10-fold cross-validation 93.1 ± 2.9 Das 2010 [39] Neural network, 35% of the data used for testing following random initial partitioning”
Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning (2020, methodology): “In [22], the performance of four classiﬁers, Decision Trees, Regression, DMneural, and Neural Networks (NN), has been compared in detecting PD, and the best accuracy of 92.9% is obtained using NN algorithm.”