Abstract
Parkinson’s Disease (PD) is one of the incurable neurodegenerative disorders. This progressive nervous system disorder mainly occurs at the age of early 60 and become worst day by day. Till now we do not have any particular medicine or surgery for this disease. As PD cannot be fully curable, so it is important to detect PD at its early stage to prevent more harm. PD detection is also important at its early stage as by the time of manifestation of clinical symptoms occur, more than 60% dopaminergic neurons lost by the time. Parkinson’s Disease detection has now become a popular field of study and research. In this chapter, we use the audio medical measurement of 42 people, who are distinguished with early-stage Parkinson’s Disease. The subjects were hired for a six months trial of a remote-controlled disease to remotely diagnose between PD and healthy people. The data were automatically recorded in subjects’ homes. We have applied Linear Regression, Polynomial Regression, Elastic-Net, Lasso, Decision Tree, k-Nearest Neighbour, Random Forest and Gradient Boosting Regression on the dataset, to achieve the model’s performance in terms of accuracy. This research will be useful in early diagnosis of Parkinson’s disease and to prevent its’ harmful impact on the patients.
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Dataset used for this work. https://archive.ics.uci.edu/ml/datasets/parkinsons+telemonitoring
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Adhikary, A., Majumder, K., Chatterjee, S., Dasgupta, A., Shaw, R.N., Ghosh, A. (2023). Detection of Parkinson’s Disease Through Telemonitoring and Machine Learning Classifiers. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_12
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DOI: https://doi.org/10.1007/978-3-031-25088-0_12
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