Prediction of neurodegenerative diseases from functional brain imaging data
Year: 2016
Type: dissertation
Abstract: Neurodegenerative diseases are a challenge, especially in the developed society where life expectancy is high. Since these diseases progress slowly, they are not easy to diagnose at an early stage. Moreover, they portray similar disease features, which makes them hard to differentiate. In this thesis, the objective was to devise techniques to extract biomarkers from brain data for the prediction and classification of neurodegenerative diseases, in particular parkinsonian syndromes. We used principal component analysis in combination with the scaled subprofile model to extract features from the brain data to classify these disorders. Thereafter, the features were provided to several classifiers, i.e., decision trees, generalized matrix learning vector quantization, and support vector machine to classify the parkinsonian syndromes. A validation of the classifiers was performed. The decision tree method was compared to the stepwise regression method which aims at linearly combining a few good principal components. The stepwise regression method performed better than the decision tree method in the classification of the parkinsonian syndromes. Combining the two methods is feasible. The decision trees helped us to visualize the classification results, hence providing an insight into the distribution of features. Both generalized matrix learning vector quantization and support vector machine are better than the decision tree method in the classification of early-stage parkinsonian syndromes. All the classification methods used in this thesis performed well with later disease stage data. We conclude that generalized matrix learning vector quantization and decision tree methods can be recommended for further research on neurodegenerative disease classification and prediction.
Author: Deborah Mudali
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2511273239