A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients

Abstract

In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

Hemodialysis Patient Death Prediction Using Logistic Regression

“…Logistic regression does not require the limitations of the independent variables. Logistic regression does not require that the independent variables be in the form of intervals [7].…”

Kernelized k-Local Hyperplane Distance Nearest-Neighbor Model for Predicting Cerebrovascular Disease in Patients With End-Stage Renal Disease

“…Our previous work identified abnormal FGF23 and Klotho levels, inflammatory status, and malnutrition were the unconventional risk factors for vascular calcification and CKD-MBD in patients with end-stage renal failure ( Maraj et al, 2018 ). Using machine learning methods to analyze the risk factors of CKD-MBD in patients with end-stage renal failure, we have found that elevated serum FGF23 levels in patients with ESRD is an independent risk factor for abdominal aortic calcification ( Liu et al, 2021 ). Recent studies have also identified FGF23 as an independent risk factor for cerebrovascular diseases in both CKD and non-CKD populations ( Wright et al, 2016 ).…”