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

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dc.citedby3
dc.contributor.authorShayea G.G.en_US
dc.contributor.authorZabil M.H.M.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorJoudar S.S.en_US
dc.contributor.authorHamid R.A.en_US
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorZahid I.A.en_US
dc.contributor.authorSharaf I.M.en_US
dc.contributor.authorid58026194100en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid57201009814en_US
dc.contributor.authorid57672501200en_US
dc.contributor.authorid57216614154en_US
dc.contributor.authorid57201013684en_US
dc.contributor.authorid57205435311en_US
dc.contributor.authorid58318020900en_US
dc.contributor.authorid17435789800en_US
dc.date.accessioned2025-03-03T07:41:38Z
dc.date.available2025-03-03T07:41:38Z
dc.date.issued2024
dc.description.abstractIn 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. Furthermore, the development of ML models must contend with two real-time scenarios: normal tests and adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge the domains of machine learning and fuzzy multicriteria decision-making (MCDM). First, the utilized dataset comprises authentic information, encompassing 19 medical and sociodemographic features from 1296 autistic patients who received autism diagnoses via the intelligent triage method. These patients were categorized into one of three triage labels: urgent, moderate, or minor. We employ principal component analysis (PCA) and two algorithms to fuse a large number of dataset features. Second, this fused dataset forms the basis for rigorously testing eight ML models, considering normal and adversarial attack scenarios, and evaluating classifier performance using nine metrics. The third phase developed a robust decision-making framework that encompasses the creation of a decision matrix (DM) and the development of the 2-tuple linguistic Fermatean fuzzy decision by opinion score method (2TLFFDOSM) for benchmarking multiple-ML models from normal and adversarial perspectives, accomplished through individual and external group aggregation of ranks. Our findings highlight the effectiveness of PCA algorithms, yielding 12 principal components with acceptable variance. In the external ranking, logistic regression (LR) emerged as the top-performing ML model in terms of the 2TLFFDOSM score (1.3370). A comparative analysis with five benchmark studies demonstrated the superior performance of our framework across all six checklist comparison points. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo151
dc.identifier.doi10.1007/s44196-024-00543-3
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85196075476
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85196075476&doi=10.1007%2fs44196-024-00543-3&partnerID=40&md5=55328181a79c4f3621b6d890454748ea
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36227
dc.identifier.volume17
dc.publisherSpringer Science and Business Media B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal of Computational Intelligence Systems
dc.subjectBenchmarking
dc.subjectClassification (of information)
dc.subjectDecision trees
dc.subjectDiagnosis
dc.subjectDiseases
dc.subjectEconomic and social effects
dc.subjectLarge datasets
dc.subjectLinguistics
dc.subjectMachine learning
dc.subjectStatistical tests
dc.subject2-tuple linguistic
dc.subject2-tuple linguistic fermatean
dc.subjectAutism spectrum disorders
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectMulticriteria decision-making
dc.subjectReal- time
dc.subjectRobust machine learning
dc.subjectSelection
dc.subjectTriage
dc.subjectPrincipal component analysis
dc.titleFuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applicationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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