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

• 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

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....