Add like
Add dislike
Add to saved papers

Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late. Recent neuroimaging techniques, along with machine learning methods, provide alternative solutions for PD screening. In this paper, we propose a novel feature selection technique, based on iterative canonical correlation analysis (ICCA), to investigate the roles of different brain regions in PD through T1-weighted MR images. First of all, gray matter and white matter tissue volumes in brain regions of interest are extracted as two feature vectors. Then, a small group of significant features were selected using the iterative structure of our proposed ICCA framework from both feature vectors. Finally, the selected features are used to build a robust classifier for automatic diagnosis of PD. Experimental results show that the proposed feature selection method results in better diagnosis accuracy, compared to the baseline and state-of-the-art methods.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app