Journal of Clinical Chemistry and Laboratory Medicine
Open Access
ISSN: 2736-6588
ISSN: 2736-6588
Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
Research Article
Machine Learning-Based Prediction of Cardiac Dysfunction in
Hemodialysis Patients through Blood Cardiovascular Proteomics
Author(s): Jen-Ping Lee, Yu-Lin Chao, Ping-Hsun Wu, Yun-Shiuan Chuang, Chan Hsu, Pei-Yu Wu, Szu-Chia Chen, Wei-Chung Tsai, Yi-Wen Chiu, Shang-Jyh Hwang, Yi-Ting Lin* and Mei-Chuan Kuo*
Objective: Cardiac function stands as a robust and seemingly independent predictor of all-cause and cardiovascular
mortality among individuals undergoing Hemodialysis (HD). The crucial need for efficient cardiac function
assessment led us to explore the potential of using accessible blood sampling for evaluation. In this study, we
cautiously harnessed cardiovascular proteomics in conjunction with Machine Learning (ML) techniques to explore
the feasibility of predicting cardiac function in HD patients.
Methods: A cohort of 328 HD patients was gathered from two units located in Southern Taiwan. Utilizing proximity
extension assays, a comprehensive measurement of 184 cardiovascular proteins was performed. Employing machine
learning, we optimized a model for predicting cardiac dysfunction based on ejection fraction. Model perform.. View More»
DOI:
10.35248/2736-6588.24.7.283