Enhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality

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dc.contributor.authorAlyasin, Eman Ibrahim
dc.contributor.authorAta, Oğuz
dc.contributor.authorMohammedqasim, Hayder
dc.contributor.authorMohammedqasem, Roa'a
dc.date.accessioned2024-02-20T12:14:10Z
dc.date.available2024-02-20T12:14:10Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractAddressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.en_US
dc.identifier.citationAlyasin, E. I., Ata, O., Mohammedqasim, H., & Mohammedqasem, R. A. (2024). Enhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality. Applied Sciences, 14(1), 356. 10.3390/app14010356en_US
dc.identifier.issn2076-3417
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85192465220
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4597
dc.identifier.volume14en_US
dc.identifier.wosWOS:001139258800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlyasin, Eman Ibrahim
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.relation.ispartofApplied Sciences - Basel
dc.relation.isversionof10.3390/app14010356en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectICF-CYen_US
dc.subjectFeature selectionen_US
dc.subjectOversamplingen_US
dc.subjectPredictive modelsen_US
dc.subjectExpert systemen_US
dc.subjectShapley value explanationen_US
dc.subjectPhysical and motor disabilityen_US
dc.subjectSelf-careen_US
dc.subjectMachine learningen_US
dc.titleEnhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality
dc.typeArticle

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