SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers

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dc.authorid0009-0003-8365-1000
dc.contributor.authorFırat, Yelda
dc.contributor.authorid41749
dc.date.accessioned2026-06-15T06:54:17Z
dc.date.available2026-06-15T06:54:17Z
dc.date.issued2026-03
dc.departmentFakülteler, Mühendislik, Mimarlık ve Tasarım Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionScience Citation Index Expanded (SCI-EXPANDED) Social Sciences Citation Index (SSCI)
dc.description.abstractIntroduction Early Autism spectrum disorder (ASD) diagnosis is critical for intervention, yet current methods rely on subjective clinical observations. This study develops objective tools to classify ASD severity using 3D motor movement analysis, addressing motor abnormalities as core diagnostic features.Methods A Random Forest (RF) model classified three severity levels using 463 motor features from 25 Kinect V2 joint points. Data from 109 children (50 typical, 50 moderate ASD, 9 severe ASD) were validated via 5-fold cross-validation and two held-out test sets (20% each). Shapley Additive Explanations (SHAP) analysis identified critical motor biomarkers.Results The model achieved 84.6 +/- 10.9% accuracy (5-fold cross-validation) and 86.4% accuracy (internal and held-out test sets). For severe ASD, the model achieved 100% classification accuracy on synthetic test data (4/4 cases; 95% CI: 39.8%-100.0%). However, this result represents a methodological proof-of-concept rather than clinical validation, as severe ASD features were synthetically generated from moderate ASD data and the model has not been validated on real Kinect-derived severe ASD motor data. SHAP analysis identified wrist movements, knee trajectories, and elbow-to-foot distances as key motor biomarkers for severity classification.Discussion This Kinect-based approach with RF and SHAP offers effective, interpretable ASD severity assessment for typical and moderate ASD classes, with promising methodological foundations for severe ASD pending validation on real data.
dc.identifier.citationFırat, Y. (2026). SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers. Frontiers Psychiatry, 17, 1-16. https://doi.org/10.3389/fpsyt.2026.1751654
dc.identifier.doi10.3389/fpsyt.2026.1751654
dc.identifier.endpage16
dc.identifier.issn1664-0640
dc.identifier.startpage1
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/427
dc.identifier.volume17
dc.identifier.wosWOS:001729632900001
dc.identifier.wosqualityQ2
dc.institutionauthorFırat, Yelda
dc.language.isoen
dc.publisherFRONTIERS MEDIA SA
dc.relation.journalFRONTIERS IN PSYCHIATRY
dc.relation.publicationcategoryMakale- Uluslararası- Hakemli Dergi- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectautism spectrum disorder
dc.subjectmotor biomarkers
dc.subjectrandom forest
dc.subjectSHAP analysis
dc.subjectviolence level classification
dc.titleSHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers
dc.typeMakale
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