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首页> 外文期刊>Journal of applied statistics >Proximal support vector machine techniques on medical prediction outcome
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Proximal support vector machine techniques on medical prediction outcome

机译：支持向量机技术在医学预测中的应用

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摘要

One of the major issues in medical field constitutes the correct diagnosis, including the limitation of human expertise in diagnosing the disease in a manual way. Nowadays, the use of machine learning classifiers, such as support vector machines (SVM), in medical diagnosis is increasing gradually. However, traditional classification algorithms can be limited in their performance when they are applied on highly imbalanced data sets, in which negative examples (i.e. negative to a disease) outnumber the positive examples (i.e. positive to a disease). SVM constitutes a significant improvement and its mathematical formulation allows the incorporation of different weights so as to deal with the problem of imbalanced data. In the present work an extensive study of four medical data sets is conducted using a variant of SVM, called proximal support vector machine (PSVM) proposed by Fung and Mangasarian [9]. Additionally, in order to deal with the imbalanced nature of the medical data sets we applied both a variant of SVM, referred as two-cost support vector machine and a modification of PSVM referred as modified PSVM. Both algorithms incorporate different weights one for each class examples.
机译：医学领域的主要问题之一是正确的诊断，包括手动诊断疾病的人类专业知识的局限性。如今，机器学习分类器（例如支持向量机（SVM））在医学诊断中的使用正在逐渐增加。然而，当将传统分类算法应用于高度不平衡的数据集时，传统分类算法的性能可能受到限制，其中阴性实例（即对疾病的阴性）超过阳性例（即对疾病的阳性）。 SVM构成了重大改进，其数学公式允许合并不同的权重，从而解决了数据不平衡的问题。在当前的工作中，使用FVM和Mangasarian提出的称为近端支持向量机（PSVM）的SVM变体对四个医学数据集进行了广泛的研究[9]。另外，为了处理医学数据集的不平衡特性，我们同时使用了SVM的变体（称为双重成本支持向量机）和PSVM的变体（称为修改后的PSVM）。两种算法都为每个类别示例合并了不同的权重。

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