Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

In the context of autism spectrum disorder (ASD) triage, the robustness of machine learning (ML) models is a paramount concern. Ensuring the robustness of ML models faces issues such as model selection, criterion importance, trade-offs, and conflicts in the evaluation and benchmarking of ML models....

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Bibliographic Details
Main Authors: Shayea G.G., Zabil M.H.M., Albahri A.S., Joudar S.S., Hamid R.A., Albahri O.S., Alamoodi A.H., Zahid I.A., Sharaf I.M.
Other Authors: 58026194100
Format: Article
Published: Springer Science and Business Media B.V. 2025
Subjects:
Benchmarking
Classification (of information)
Decision trees
Diagnosis
Diseases
Economic and social effects
Large datasets
Linguistics
Machine learning
Statistical tests
2-tuple linguistic
2-tuple linguistic fermatean
Autism spectrum disorders
Machine learning models
Machine-learning
Multicriteria decision-making
Real- time
Robust machine learning
Selection
Triage
Principal component analysis
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