@inproceedings{7e172492d4934d6683948840e942074a,
title = "Ensemble methods for prediction of Parkinson disease",
abstract = "Parkinson disease is a degenerative disorder of the central nervous system. In the present paper, we study the effectiveness of regression tree ensembles to predict the presence and severity of symptoms from speech datasets. This is a regression problem. Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to convert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we also study a recently developed RvC ensemble method for the prediction of Parkinson disease. Experimental results suggest that the RvC ensembles perform better than a single regression tree. Experiments also suggest that regression tree ensembles created using bagging procedure can be a useful tool for predicting Parkinson disease. The RvC ensembles and regression tree ensembles performed similarly on the dataset.",
keywords = "Decision trees, Ensembles, Parkinson disease, Regression trees",
author = "Halawani, {Sami M.} and Amir Ahmad",
year = "2012",
doi = "10.1007/978-3-642-32639-4_63",
language = "English",
isbn = "9783642326387",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "516--521",
booktitle = "Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings",
note = "13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012 ; Conference date: 29-08-2012 Through 31-08-2012",
}