Abstract: Intradialytic hypotension (IDH) is a common complication in patients undergoing maintenance hemodialysis and is associated with an increased risk of cardiovascular and all-cause mortality. Machine learning (ML) and deep learning (DL) techniques transform healthcare by enabling accurate disease diagnosis, personalised treatment plans, and clinical decision support. However, challenges like data quality, privacy, and interpretability must be addressed for responsible adoption. This survey review aims to summarise and analyse relevant articles on applying machine learning models for predicting IDH. Among these models, deep learning, a subfield of machine learning, stands out because it can improve the overall performance of health care, particularly in diagnostic imaging and pathologic processes and in the synthetic judgment of big data flow. The insights gained from this survey review will assist researchers and practitioners in selecting appropriate machine-learning models and implementing preemptive measures to prevent IDH in dialysis patients.

Saeed Alqahtani, Suhuai Luo, Mashhour Alanazi, Kamran Shaukat, Mohammed G Alsubaie and Mohammad Amer, “Machine Learning for Predicting Intradialytic Hypotension: A Survey Review” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151031

@article{Alqahtani2024,
title = {Machine Learning for Predicting Intradialytic Hypotension: A Survey Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151031},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151031},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Saeed Alqahtani and Suhuai Luo and Mashhour Alanazi and Kamran Shaukat and Mohammed G Alsubaie and Mohammad Amer}
}