Open Access

ARTICLE

Artificial Neural Network-Based Risk Assessment for Cardiac Implantable Electronic Device Complications

Chih-Yin Chien^1,2, Tsae-Jyy Wang¹, Pei-Hung Liao¹, Ying-Hsiang Lee^3,4,5,*, Wei-Sho Ho^6,7,*

1 School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei City, 112303, Taiwan
2 Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, Taipei City, 112021, Taiwan
3 Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, Taipei City, 112021, Taiwan
4 Cardiovascular Medicine, MacKay Memorial Hospital, Taipei City, 104217, Taiwan
5 Department of Medicine, MacKay Medical University, New Taipei City, 251404, Taiwan
6 Department of Electrical and Mechanical Technology, National Changhua University of Education Bao-Shan Campus, Changhua City, 500208, Taiwan
7 NCUE Alumni Association, National Changhua University of Education Jin-De Campus, Changhua County, Changhua City, 500207, Taiwan

* Corresponding Authors: Ying-Hsiang Lee. Email: ; Wei-Sho Ho. Email:

Congenital Heart Disease 2025, 20(5), 601-612. https://doi.org/10.32604/chd.2025.072431

Received 27 August 2025; Accepted 04 November 2025; Issue published 30 November 2025

Abstract

Background: Cardiac implantable electronic devices (CIEDs) are essential for preventing sudden cardiac death in patients with cardiovascular diseases, but implantation procedures carry risks of complications such as infection, hematoma, and bleeding, with incidence rates of 3–4%. Previous studies have examined individual risk factors separately, but integrated predictive models are lacking. We compared the predictive performance and interpretability of artificial neural network (ANN) and logistic regression models to evaluate their respective strengths in clinical risk assessment. Methods: This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device (CIED) implantation in Taiwan between 2017 and 2018. To address class imbalance and enhance model training, the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique (SMOTE). A total of 13 clinical risk factors were evaluated (e.g., age, body mass index (BMI), platelet count, left ventricular ejection fraction (LVEF), prothrombin time/international normalized ratio (PT/INR), hemoglobin (Hb), comorbidities, and antithrombotic use). Results: The most influential risk factors identified by the ANN model were platelet count, PT/INR, LVEF, Hb, and age. In the logistic regression analysis, reduced LVEF, lower hemoglobin levels, prolonged PT/INR, and lower BMI were significantly associated with an increased risk of complications. ANN model achieved a higher area under the curve (AUC = 0.952) compared to the logistic regression model (AUC = 0.802), indicating superior predictive performance. Additionally, the overall model quality was also higher for the ANN model (0.93) than for logistic regression (0.76). Conclusions: This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors. These findings support the use of ANN-based models for individualized risk stratification, enhancing procedural safety, improving patient outcomes, and potentially reducing healthcare costs associated with postoperative complications.

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