A novel ensemble XGBoost and deep Q-network for pregnancy risk prediction on multi-class imbalanced datasets
Kurnianingsih Kurnianingsih, Sou Nobukawa, Melyana Nurul Widyawati, Cipta Pramana, Nurseno Bayu Aji, Afandi Nur Aziz Thohari, Dwiana Hendrawati, Eri Sato-Shimokawara, Naoyuki Kubota
Abstract
Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5313 pregnant women in Indonesia and showing superior results on public datasets.