A novel ensemble decision tree classifier using hybrid feature selection measures for Parkinson's disease prediction. (2018)

• Record Type: Journal Article
• Authors: Kadaru, Bala Brahmeswara; Reddy, B. Raja Srinivasa
• Is Part Of: International journal of data science. Volume 3:Number 4(2018)
• Page Start: 289
• Page End: 307
• Publication Date: 2018
• Subjects: feature selection measures -- ensemble decision tree -- disease prediction; Big data -- Periodicals; Data mining -- Periodicals; Information storage and retrieval systems -- Periodicals; Decision making -- Data processing -- Periodicals; 005.7
• Languages: English
• ISSNs: 2053-0811

Abstract

Parkinson's disease and Alzheimer's disease are the most critical health issues in current days. In neurology, Parkinson disease affects the dopamine receptors of central nervous system. It affects the movement of patients. Dopamine cells are degenerated in this disease progressively, which leads to rapid growth of severity. Extensive amount of research works were done since years for prediction of Parkinson's disease in the early stage. Till date, there is no significant approach, which will provide optimised performance for prediction. Alzheimer's disease is another neurological disease, which generally leads to dementia in most cases Machine learning approaches are more promising approaches for the prediction of these above-said diseases. We presented a novel ensemble-based feature selection measure and decision tree model to predict Parkinson's disease. Experimental results proved that the proposed model has high computational accuracy and true positive rate compared with traditional feature selection measures and ensemble decision trees.