Prediction of Second Neurological Attack in Patients with Clinically Isolated Syndrome Using Support Vector Machines

Viktor Wottschel, O. Ciccarelli, D. Chard, David H. Miller, D. Alexander
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引用次数: 2

Abstract

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy.
应用支持向量机预测临床孤立综合征患者神经系统二次发作
本研究的目的是利用支持向量机预测从临床孤立综合征到临床明确多发性硬化症的转化。根据73例患者的基线数据计算出的特征,将转换者和非转换者分为两组。数据包括标准磁共振图像、二值病变掩模以及临床和人口统计信息。计算了15个特征,并使用多项式核函数和径向基函数迭代测试了所有特征组合的预测能力,并进行了留一交叉验证。该预测的准确率高达86.4%,敏感性和特异性在相同的范围内,表明该方法对于临床孤立综合征患者的第二次临床发作预测是可行的,并且所选择的特征是合适的。这两个特征的性别和发病病变的位置已被用于所有的特征组合,导致高精度,表明他们是高度预测。然而,有必要添加支持功能以最大限度地提高准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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