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首页> 外文期刊>Mineralogia >Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure
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Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure

机译：交叉验证方法对充血性心力衰竭诊断分类器性能的影响比较

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Congestive heart failure (CHF) occurs when the heart is unable to provide sufficient pump action to maintain blood flow to meet the needs of the body. Early diagnosis is important since the mortality rate of the patients with CHF is very high. There are different validation methods to measure performances of classifier algorithms designed for this purpose. In this study, k-fold and leave-one-out cross-validation methods were tested for performance measures of five distinct classifiers in the diagnosis of the patients with CHF. Each algorithm was run 100 times and the average and the standard deviation of classifier performances were recorded. As a result, it was observed that average performance was enhanced and the variability of performances was decreased when the number of data sections used in the cross-validation method was increased.
机译：当心脏无法提供足够的泵动作来维持血液流动以满足身体需要时，就会发生充血性心力衰竭（CHF）。由于CHF患者的死亡率很高，因此早期诊断很重要。有不同的验证方法来衡量为此目的设计的分类器算法的性能。在这项研究中，测试了k折和留一法交叉验证方法对CHF患者诊断中五个不同分类器的性能指标。每个算法运行100次，并记录分类器性能的平均值和标准偏差。结果，观察到，当交叉验证方法中使用的数据部分的数量增加时，平均性能得到提高，性能的可变性降低。

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