Citation

Babu, Tina and Nair, Rekha R. and Thrilok, Kolla and Khoh, Wee How and Nair Mogan, Jashila (2026) Explainable Machine Learning for Kawasaki Disease Prediction Using Embedded Feature Selection and SHAP-Based Interpretability Analysis. Procedia Computer Science, 283. pp. 3676-3685. ISSN 18770509

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Abstract

Kawasaki disease (KD) is an acute systemic vasculitis principally affecting children below the age of 5 years, characterized by persistent fever and possible cardiovascular complications. Due to nonspecific clinical presentations and lack of validated biomarkers, its early diagnosis remains a significant challenge. This study proposes a machine learning framework for KD prediction, incorporating embedded feature selection via gradient boosting machines, multi-algorithm ensemble modeling, and SHAP-based explainability analysis. A dataset comprising 645 patients with 84 clinical and laboratory features was obtained from the Pediatric Heart Network. Six classifiers were evaluated: Logistic Regression, LightGBM, Gradient Boosting, Explainable Boosting, AdaBoost, and Decision Tree. Logistic Regression achieved superior performance with an AUC-ROC of 91%, accuracy of 87%, precision of 88%, recall of 91%, and F1-score of 89%. Key predictive features included cervical lymphadenopathy, edema of the hands, and rash, with SHAP analysis confirming their clinical relevance. The framework integrates feature selection with interpretability, equipping clinicians with transparent and trustworthy diagnostic support. This work demonstrates the potential of explainable AI to enhance early KD diagnosis and clinical decision-making

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Gradient Boosting
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Sep 2026 09:06
Last Modified:	03 Sep 2026 09:06
URII:	http://shdl.mmu.edu.my/id/eprint/16650

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