Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023, Wuxi, China.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China.
Comput Math Methods Med. 2021 Jul 27;2021:2464821. doi: 10.1155/2021/2464821. eCollection 2021.
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.
在终末期肾病（ESRD）中，血管钙化的危险因素对血液透析患者的生存至关重要。为了有效评估血管钙化的程度，可以使用机器学习算法预测 ESRD 患者的血管钙化风险。由于不同风险水平下收集的数据量不平衡，因此会对分类任务产生影响。因此，本工作提出了一种基于自表示的有效模糊支持向量机（FSVM-SR）来预测血管钙化风险。此外，还将我们的方法与其他常规机器学习方法进行了比较，结果表明，我们的方法可以更好地完成血管钙化风险的分类任务。
