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2021 | OriginalPaper | Buchkapitel

29. Identification of Parkinson’s Disease Using Machine Learning and Neural Networks

verfasst von : Ved Abhyankar, Rushikesh Tapdiya

Erschienen in: Intelligent Manufacturing and Energy Sustainability

Verlag: Springer Singapore

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Abstract

Parkinson’s Disease (PD) continues to affect millions of people worldwide, with as many as a million in the US and roughly 60,000 diagnosed each year. Early detection of PD can help in better handling the finances of the treatment as well as being substantially better for the patient’s quality of life. Artificial Neural Networks and Machine Learning techniques can greatly help with the early diagnosis and prediction of PD. In this paper we have used several techniques for the classification of the test subjects as having or not having PD based on their biomedical voice samples. After comparing the techniques based on Accuracy, recall, f1 score and precision, the best performance has been obtained by using SVM along with PCA. Doctors can thus classify patients appropriately with the help of this analysis.

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Titel: Identification of Parkinson’s Disease Using Machine Learning and Neural Networks
verfasst von: Ved Abhyankar
Rushikesh Tapdiya
Verlag: Springer Singapore
Buch: Intelligent Manufacturing and Energy Sustainability
Print ISBN: 978-981-334-442-6

Electronic ISBN: 978-981-334-443-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-33-4443-3_29

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