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Combining machine learning and resampling strategies can counteract imbalanced data resulting from the low incidence of AITD in the real world. Tree-based ensemble learning methods, such as extreme gradient boosting (XGBoost) and adaptive boosting (Ada Boost), are commonly applied for imbalance classification [31,32]. K-nearest neighbors (KNN) with data resampling methods perform well in imbalance classification [33].
J Med Internet Res 2023;25:e43734
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