A Multi-Stage Framework for Kawasaki Disease Prediction Using Clustering-Based Undersampling and Synthetic Data...

Area of Science:

• Pediatric Rheumatology
• Artificial Intelligence in Medicine
• Diagnostic Accuracy

Background:

• Kawasaki disease (KD) is a critical pediatric vasculitis with challenging diagnosis due to varied symptoms.
• Delayed diagnosis of KD can lead to severe cardiovascular complications.
• AI leveraging routine laboratory tests offers a potential solution for early KD detection.

Purpose of the Study:

• To develop and validate a multi-stage AI framework for enhanced early detection of Kawasaki disease.
• To address the challenge of class imbalance in AI models for rare diseases like KD.
• To improve the diagnostic accuracy and clinical utility of AI in identifying KD.

Main Methods:

• A multi-stage AI framework incorporating clustering-based undersampling, data augmentation, and stacking ensemble learning was developed.
• The model was trained and tested on a large dataset (n=74,641) from Chang Gung Memorial Hospital (CGMH).
• External validation was performed on an independent dataset (n=1582) from Kaohsiung Medical University Hospital (KMUH) to ensure generalizability.

Main Results:

• The AI model achieved 95% recall with 97.5% specificity and a 53.6% F1-score on the internal test set (CGMH).
• On the external validation set (KMUH), the model demonstrated 74.7% specificity and a 23.4% F1-score at 95% recall.
• The framework successfully maintained high specificity while prioritizing sensitivity, indicating practical predictive performance.

Conclusions:

• The proposed AI framework shows significant promise for improving the early detection of Kawasaki disease.
• The model's ability to balance sensitivity and specificity makes it a valuable tool for real-world KD screening.
• This AI-driven approach can aid clinicians in overcoming diagnostic challenges associated with Kawasaki disease.