Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
Comput Biol Med. 2013 Oct;43(10):1523-9. doi: 10.1016/j.compbiomed.2013.05.024. Epub 2013 Jun 6.
Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.
糖尿病（DM）影响了世界上相当数量的人，并且每年的病例数都在增加。由于与疾病的遗传基础有很强的联系，因此极难治愈。然而，可以控制它以防止严重的后果，如器官损伤。因此，糖尿病的诊断和治疗监测非常重要。在本文中，我们提出了一种用于 DM 的非侵入性诊断支持系统。该系统通过使用心率变异性（HRV）分析来确定患者的心脏健康状况，从而确定是否存在糖尿病。该分析基于九个非线性特征，分别是：近似熵（ApEn）、最大 Lyapunov 指数（LLE）、去趋势波动分析（DFA）和递归量化分析（RQA）。临床意义显著的指标被用作分类算法的输入，分别是：AdaBoost、决策树（DT）、模糊 Sugeno 分类器（FSC）、k-最近邻算法（k-NN）、概率神经网络（PNN）和支持向量机（SVM）。十折分层交叉验证用于选择最佳分类器。AdaBoost 算法，以最小二乘法（LS）作为弱学习器，表现优于其他分类器，平均准确率为 90%，灵敏度为 92.5%，特异性为 88.7%。
