Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study
Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study
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DOI：
10.4178/epih.e2016011
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发表时间：
2016-01-01
影响因子：
3.8
通讯作者：
Faradmal, Javad
中科院分区：
文献类型：
--
作者：
Kazemi, Maryam;Moghimbeigi, Abbas;Faradmal, Javad
糖尿病：糖尿病在世界范围内的流行率正在上升，接近流行病的水平。糖尿病神经病变是糖尿病最常见的并发症之一，是一种严重的疾病，可导致截肢。本研究采用多类别支持向量机（MSVM）预测糖尿病周围神经病变的严重程度分为四类，使用患者的人口统计学特征和临床features.METHODS：在这项研究中，数据收集在伊朗的哈马丹糖尿病中心。采用方便抽样方法入组患者。招募了600名患者。在获得知情同意后，进行了收集一般信息的问卷调查和神经病残疾评分（NDS）问卷调查。NDS用于对疾病的严重程度进行分类。我们使用MSVM结合一对一和一对一方法以及三个核函数（径向基函数（RBF）、线性和多项式）来预测具有不平衡数据集的疾病类别。采用合成少数类过采样技术提高模型性能。为了比较模型的性能，使用准确性的平均值。结果：对于预测糖尿病神经病变，从平衡数据集和RBF核函数构建的分类器采用一对一策略预测患者所属的类别，准确率约为76%。这项研究的结果表明，在整体分类准确性方面，基于平衡数据集的MSVM模型可用于预测糖尿病神经病变的严重程度，并应进一步研究用于其他疾病的预测。
OBJECTIVES: Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients' demographic characteristics and clinical features.METHODS: In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used.RESULTS: For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy.CONCLUSIONS: The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.