Early diagnosis of Parkinson disease using Machine Learning Techniques
Article No.: 99, Pages 1 - 8
Abstract
Approximately two in every thousand people suffer from Parkinson disease. The symptoms of this neurological disorder can be motor or non-motor. However, it is significantly difficult to determine the gravity and appropriate classification of the disease. The diagnosis of PD majorly depends on the clinical examination and neurological examinations. Recently, machine learning techniques have proved to be an alternate method to detect the disease in a very early stage of it. Machine learning techniques use motor symptoms (gait analysis, handwriting, etc.) and non-motor symptoms (voice characteristics) to classify the people suffering and not suffering from PD. This study evaluated classifiers such as K-Nearest Neighbours (K-NN), Random Forest (RF), Gradient Boosting, Support Vector Machine (SVM), Boosting, and Bagging.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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