Machine Learning Methods for Brain Network Classification: Application
to Autism Diagnosis using Cortical Morphological Networks
- URL: http://arxiv.org/abs/2004.13321v1
- Date: Tue, 28 Apr 2020 06:23:29 GMT
- Title: Machine Learning Methods for Brain Network Classification: Application
to Autism Diagnosis using Cortical Morphological Networks
- Authors: Ismail Bilgen and Goktug Guvercin and Islem Rekik
- Abstract summary: We leverage crowdsourcing to build a pool of machine learning pipelines for neurological disorder diagnosis with application to autism spectrum disorder (ASD) diagnosis.
The first-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity, while the second-ranked team achieved 63.8%, 62.5%, 65% respectively.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autism spectrum disorder (ASD) affects the brain connectivity at different
levels. Nonetheless, non-invasively distinguishing such effects using magnetic
resonance imaging (MRI) remains very challenging to machine learning diagnostic
frameworks due to ASD heterogeneity. So far, existing network neuroscience
works mainly focused on functional (derived from functional MRI) and structural
(derived from diffusion MRI) brain connectivity, which might not capture
relational morphological changes between brain regions. Indeed, machine
learning (ML) studies for ASD diagnosis using morphological brain networks
derived from conventional T1-weighted MRI are very scarce. To fill this gap, we
leverage crowdsourcing by organizing a Kaggle competition to build a pool of
machine learning pipelines for neurological disorder diagnosis with application
to ASD diagnosis using cortical morphological networks derived from T1-weighted
MRI. During the competition, participants were provided with a training dataset
and only allowed to check their performance on a public test data. The final
evaluation was performed on both public and hidden test datasets based on
accuracy, sensitivity, and specificity metrics. Teams were ranked using each
performance metric separately and the final ranking was determined based on the
mean of all rankings. The first-ranked team achieved 70% accuracy, 72.5%
sensitivity, and 67.5% specificity, while the second-ranked team achieved
63.8%, 62.5%, 65% respectively. Leveraging participants to design ML diagnostic
methods within a competitive machine learning setting has allowed the
exploration and benchmarking of wide spectrum of ML methods for ASD diagnosis
using cortical morphological networks.
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