editedbook
Publication: IIP Proceedings Year: 2022, Month: November Page No: 267-277, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023343CC87043A0ED6.pdf
A COMPARITIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF PARKINSON’S DIEAESE AT AN EARLY STAGE
Area/Stream: Computing Technologies and Data Sciences, Authors: Dr. Anil Kumar D Keywords: Machine learning Algorithms, Parkinson's dieaese Book Name /series: A COMPARITIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF PARKINSON’S DIEAESE AT AN EARLY STAGEPublication: IIP Proceedings Year: 2022, Month: November Page No: 267-277, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023343CC87043A0ED6.pdf
Abstract:
Parkinson's disease (PD) is one among many leading public health diseases in the world. This disese has impacted many people and is alarmingly increasing. Thus, it is very important to predict it at an early stage and has been a difficult task among researchers as the symptoms of the disease are evident in either mid or late stages. Thus, this chapter concentrates on the symptoms of speech articulation difficulty of persons with PD and formulates the model using various machine learning like support vector machine, decision tree, random decision forest, and linear regression, adaptive boosting, bagging, neural networks. The performance of these graders is assessed through various measurements, e.g. Accuracy, receptor operating characteristics (ROC) curve, sensitivity, Precision, as well as specificity. Finally, xgboost is used to find the most important features of any feature to predict Parkinson's disease.
Cite this: Dr. Anil Kumar D,"A COMPARITIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF PARKINSON’S DIEAESE AT AN EARLY STAGE", A COMPARITIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF PARKINSON’S DIEAESE AT AN EARLY STAGE, November, 2022, 267-277, 978-81-959356-3-5, https://rsquarel.org/assets/docupload/rsl2023343CC87043A0ED6.pdf
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