Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

D. Mudali, L. K. Teune, R. J. Renken, K. L. Leenders, J. B. T. M. Roerdink*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)
243 Downloads (Pure)

Abstract

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple systematrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Original languageEnglish
Article number136921
Number of pages10
JournalComputational and Mathematical Methods in Medicine
Volume2015
DOIs
Publication statusPublished - 2015

Keywords

  • EMISSION TOMOGRAPHIC DATA
  • MULTICLASS CLASSIFICATION
  • DIFFERENTIAL-DIAGNOSIS
  • FUNCTIONAL PATTERNS
  • PERMUTATION TESTS
  • DISEASE
  • C4.5
  • CRITERIA
  • MODEL

Cite this