Classification of Parkinson’s Disease Using Data Mining Techniques
Journal Title: Journal of Parkinson’s Disease and Alzheimer’s Disease - Year 2015, Vol 2, Issue 1
Abstract
Parkinson’s disease is a human disease caused by tolerant disorder of the nervous system that affects movement. It grows slowly, occasionally initiated with a scarcely visible tremor in just one hand. But while a tremor may be the most well-known sign of Parkinson’s disease, the disorder also commonly causes stiffness or slowing of movement. Cluster analysis is an iterative process to modify data preprocessing and model parameters until the result achieves the desired properties. Cluster analysis can be realized by a number of algorithms that varysignificantly in their conception to set up a cluster and how to efficiently mark them. Some of the common conceptions of these algorithms are their pre-defined vital features. Parkinson Data Set is obtained from the UCI repository; the data is passed over the data preprocessing phases e.g. data cleaning, recovering missing values and transformed before applying three clustering techniques e.g. KNN, Random Forest, Ada-Boost. The objective of the research effort is to get an accurate model for disease detection.
Authors and Affiliations
Sajid Ullah Khan
Keywords
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- EP ID EP207470
- DOI 10.13188/2376-922X
- Views 102
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