Explainable AI feature importance analysis of CTGAN-DC model for Kawasaki Disease prediction using SHapley Additive exPlanations (SHAP).

Explainable AI feature importance analysis of CTGAN-DC model for Kawasaki Disease prediction using SHapley Additive exPlanations (SHAP).

<p>Explainable AI feature importance analysis of CTGAN-DC model for Kawasaki Disease prediction using SHapley Additive exPlanations (SHAP).</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Chuan-Sheng Hung (20489202) (author)
مؤلفون آخرون: Chun-Hung Richard Lin (6876671) (author), Jain-Shing Liu (20489205) (author), Shi-Huang Chen (20489208) (author), Tsung-Chi Hung (20489211) (author), Chih-Min Tsai (10023923) (author)
منشور في: 2024
الموضوعات:
Genetics
Immunology
Cancer
Infectious Diseases
Plant Biology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
various healthcare institutions
two major hospitals
original data characteristics
inherent data imbalance
improve data balance
first approach proposes
expanding data diversity
coronary artery complications
complicates accurate prediction
bias commonly observed
based oversampling techniques
effectively balances positive
advancing disease prediction
stacking model termed
traditional models toward
taiwan </ p
negative kd samples
enhancing prediction accuracy
predictive accuracy
effectively resolving
disease classifier
model retains
jama model
combined model
young children
thereby mitigating
statistical methods
solid foundation
significantly outperform
severe cases
predictive performance
potentially leading
majority class
including xgboost
frequently misdiagnosed
findings lay
enhancing generalization
dc model
common fever
clinical settings
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