Title: A novel ensemble decision tree classifier using hybrid feature selection measures for Parkinson's disease prediction
Authors: Bala Brahmeswara Kadaru; B. Raja Srinivasa Reddy
Addresses: Department of Computer Science and Engineering, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh – 521356, India ' CSE Department, Ram Chandra College of Engineering, Eluru, Andhra Pradesh, 534007, India
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.
Keywords: feature selection measures; ensemble decision tree; disease prediction.
International Journal of Data Science, 2018 Vol.3 No.4, pp.289 - 307
Received: 01 Apr 2017
Accepted: 15 Jun 2017
Published online: 16 Nov 2018 *