An effective model for predicting serum albumin level in hemodialysis patients. (January 2022)

• Record Type: Journal Article
• Authors: Hu, Jiao; Liu, Yi; Heidari, Ali Asghar; Bano, Yasmeen; Ibrohimov, Alisherjon; Liang, Guoxi; Chen, Huiling; Chen, Xumin; Zaguia, Atef; Turabieh, Hamza
• Is Part Of: Computers in biology and medicine. Volume 140(2022)
• Publication Date: 2022-01
• Subjects: Hemodialysis -- Serum albumin -- Swarm intelligence -- Feature selection -- Machine learning -- Fuzzy k-nearest neighbor; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105054
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients. Highlights: An effective diagnostic tool for HD blood specimens is developed. A promising model using Fuzzy KNN based on mutation quantum GWO (MQGWO) is proposed. The most critical features are effectively identified using the MQGWO feature selection tool. MQGWO performs well on a variety of different machine learning classifiers.