RFE and the learning curve for the SVM model are depicted in (A) and (B), respectively.

RFE and the learning curve for the SVM model are depicted in (A) and (B), respectively.

<p>The best performance is achieved with a total of 310 characteristics, as shown by (A). In (B), the learning curve is presented for the training Accuracy (blue) and test Accuracy (green) using the entire dataset (600 connectivity matrices subjects). The highest performance was achieved with...

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Bibliographic Details
Main Author: Caroline L. Alves (14271413) (author)
Other Authors: Tiago Martinelli (10402035) (author), Loriz Francisco Sallum (19865127) (author), Francisco Aparecido Rodrigues (19865130) (author), Thaise G. L. de O. Toutain (19865133) (author), Joel Augusto Moura Porto (19865136) (author), Christiane Thielemann (14271419) (author), Patrícia Maria de Carvalho Aguiar (19865139) (author), Michael Moeckel (19865142) (author)
Published: 2024
Subjects:
Medicine
Cell Biology
Neuroscience
Biotechnology
Science Policy
Mental Health
Biological Sciences not elsewhere classified
totaling 120 subjects
targeted intervention difficult
surpassing existing benchmarks
observed connectivity patterns
brain network integration
autism spectrum disorder
achieve superior accuracy
brain regions critical
leveraging multiclass classification
asd show disruptions
ml classification rests
multiclass classification
regions involved
typically developed
three groups
segregation among
promising avenue
overlapping symptoms
impulse control
findings pave
established practices
enhanced diagnostics
cognitive functions
clinical symptoms
accurate diagnosis
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Summary:<p>The best performance is achieved with a total of 310 characteristics, as shown by (A). In (B), the learning curve is presented for the training Accuracy (blue) and test Accuracy (green) using the entire dataset (600 connectivity matrices subjects). The highest performance was achieved with 450 connectivity matrices subjects without requiring the entire dataset. Notably, the connectivity matrices were generated using the data augmentation technique sliding window, and 600 connectivity matrices were used in total in the ML approach before the sampling technique.</p>

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