Confusion matrices depict the performance of various ML algorithms.

Confusion matrices depict the performance of various ML algorithms.

<p>The elements in the figure labeled A to F correspond to LSTM, CNN, LR, SVM, MLP, and NB, respectively. The diagonal elements represent TP values, showcasing each algorithm’s accuracy in correctly identifying positive instances. This is noteworthy on a test sample containing 180 instances.&l...

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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 elements in the figure labeled A to F correspond to LSTM, CNN, LR, SVM, MLP, and NB, respectively. The diagonal elements represent TP values, showcasing each algorithm’s accuracy in correctly identifying positive instances. This is noteworthy on a test sample containing 180 instances.</p>

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