InSeo Song, SeMo Yang, KangYoon Lee, Journal of Internet Computing and Services, Vol. 25, No. 2, pp. 29-38, Apr. 2024
10.7472/jksii.2024.25.2.29, Full Text:  HTML
Keywords: Machine Learning, Transcatheter Aortic Valve Replacement(TAVR), Permanent Pacemaker Implantation(PPI)

Abstract

After Transcatheter Aortic Valve Replacement (TAVR), comprehensive management of complications, including the need for Permanent Pacemaker Implantation (PPI), is crucial, increasing the demand for accurate prediction models. Departing from traditional image-based methods, this study developed an optimal PPI prediction model based on ECG data using the XGBoost algorithm. Focusing on ECG signals like DeltaPR and DeltaQRS as key indicators, the model effectively identifies the correlation between conduction disorders and PPI needs, achieving superior performance with an AUC of 0.91. Validated using data from two hospitals, it demonstrated a high similarity rate of 95.28% in predicting PPI from ECG characteristics. This confirms the model's effective applicability across diverse hospital data, establishing a significant advancement in the development of reliable and practical PPI prediction models with reduced dependence on human intervention and costly medical imaging.

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