Enhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality
Yükleniyor...
Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Addressing 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.
Açıklama
Anahtar Kelimeler
ICF-CY, Feature selection, Oversampling, Predictive models, Expert system, Shapley value explanation, Physical and motor disability, Self-care, Machine learning
Kaynak
Applied Sciences - Basel
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
14
Sayı
1
Künye
Alyasin, 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/app14010356
