Ensemble methods for prediction of Parkinson disease

Sami M. Halawani, Amir Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings
Pages516-521
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012 - Natal, Brazil
Duration: Aug 29 2012Aug 31 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012
Country/TerritoryBrazil
CityNatal
Period8/29/128/31/12

Keywords

  • Decision trees
  • Ensembles
  • Parkinson disease
  • Regression trees

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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