Abstract
In recent years, there is an importance for the analysis of ageing neurological diseases like Parkinson’s disease (PD) and Alzheimer’s. PD occupies the second place in Neurodegenerative disease where Alzheimer’s disease is in a fist. In this research, we collected the voice records in vowel sounds (/a, /e, /i, /o, /u) from Andhra Pradesh, India during 2016–2018 with 1200 records of 46 PD patients and 38 non-PD people between the age of 56–84. The voice data is normalized with FFT algorithm and removes the noise of the voice and construct the CSV file for analysis and predicting the PD with ANN (Artificial Neural Network). The good statistical voice parameter results give detailed information about PD and its identification. Artificial Neural Networks classifies the PD dataset with 100% training accuracy with 10 hidden neurons. In this, we have also observed accuracy of the ANN for this PD dataset 6 to 10 hidden neurons. At 10 hidden neurons, the data set performance is in peak that accuracy is 100 and R value is 1 with least iterations 264 compared to others (hidden neurons 6, 7, 8 and 9 took 1000 and above 1000 epochs and accuracy is less than 99%) within the time of 16 s for prediction of PD.
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Acknowledgements
We would like to thank the Director Professor V. V. Nageswara Rao, Principal Dr. K. B. Madhu Sahu, and management of AITAM College for encouraging and supporting us. We are thanking to PD and non-PD patients for cooperation to collect the voice data from them. The data that support the findings of this study are available in various hospitals in the state of Andhra Pradesh, India. A standard ethical committee has approved this data set and the dataset has no conflict of interest/ethical issues with any other public source or domain.
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PanduRanga Vital, T., Murty, G.S., Yogiswara Rao, K., Sriram, T.V.S. (2020). Empirical Study and Statistical Performance Analysis with ANN for Parkinson’s Vowelized Data Set. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_64
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DOI: https://doi.org/10.1007/978-981-13-8676-3_64
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