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A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis

机译:基于机器学习的透析患者心血管疾病爆发的方法

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Background and Objective: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.Methods: To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the first is an Italian dataset obtained from the Istituto di Fisiologia Clinica of Consiglio Nazionale delle Ricerche of Reggio Calabria; the second is an American dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) repository. From each one we obtained 5 datasets, according to the outcome of interest. We tested different types of algorithm (both linear and non-linear), but the final choice was to use Support Vector Machine. In particular, we obtained the best performances using the non-linear SVC with RBF kernel algorithm, optimizing it with GridSearch. The last is an algorithm useful to search the best combination of hyper-parameters (in our case, to find the best couple (C, y)), in order to improve the accuracy of the algorithm.
机译:背景和目的:患有末期肾病(ESKD)的患者具有独特的心血管风险。本研究旨在预测透析患者的一定的精确性,死亡和心血管疾病。方法:要实现我们的目的,已经使用了机器学习技术。已经考虑了两个数据集:第一个是从雷尼奥卡拉布里亚的Consiglio Nazionale Delle Ricerche的Istituto di Fisiologia Clinica获得的意大利数据集;第二个是美国国家糖尿病和消化和肾病(Niddk)存储库提供的美国数据集。根据感兴趣的结果,我们从每个我们获得5个数据集。我们测试了不同类型的算法(线性和非线性),但最终选择是使用支持向量机。特别是,我们使用带有RBF内核算法的非线性SVC获得最佳性能,并使用GridSearch优化它。最后是一种有用的算法,可用于搜索超参数的最佳组合(在我们的情况下,找到最佳耦合(C,Y)),以提高算法的准确性。

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