Diabetes mellitus (DM) is a multifactorial disease characterized by hyperglycemia. The type 1 and type 2 DM are two different conditions with insulin deficiency and insulin resistance, respectively. It may cause atherosclerosis, stroke, myocardial infarction and other relevant complications. It also features neurological degeneration with autonomic dysfunction to meet metabolic demand. The autonomic balance controls the physiological variables that exhibit nonlinear dynamics. Thus, in current work, nonlinear heart rate variability (HRV) parameters in prognosis of diabetes using artificial neural network (ANN) and support vector machine (SVM) have been demonstrated. The digital lead-I electrocardiogram (ECG) was recorded from male Wister rats of 10–12 week of age and 200 ± 20 gm of weight from control (n = 5) as well as from Streptozotocin induced diabetic rats (n = 5). A total of 526 datasets were computed from the recorded ECG data for evaluating thirteen nonlinear HRV parameters and used for training and testing of ANN. Using these parameters as inputs, the classification accuracy of 86.3% was obtained with an ANN architecture (13:7:1) at learning rate of 0.01. While relatively better accuracy of 90.5% was observed with SVM to differentiate the diabetic and control subjects. The obtained results suggested that nonlinear HRV parameters show distinct changes due to diabetes and hence along with machine learning tools, these can be used for development of noninvasive low-cost real-time prognostic system in predicting diabetes using machine learning techniques.

糖尿病（DM）是一种以高血糖为特征的多因素疾病。1型和2型DM分别是胰岛素缺乏和胰岛素抵抗的两种不同状况。它可能引起动脉粥样硬化，中风，心肌梗塞和其他相关并发症。它还具有神经功能退化和自主神经功能紊乱，以满足代谢需求。自主平衡控制表现出非线性动力学的生理变量。因此，在当前的工作中，已经证明了使用人工神经网络（ANN）和支持向量机（SVM）在糖尿病预后中的非线性心率变异性（HRV）参数。数字铅I心电图（ECG）记录于10-12周龄雄性Wister大鼠，体重为对照组的200±20 gm（n = 5）以及链脲佐菌素诱导的糖尿病大鼠（n  = 5）。从记录的ECG数据中总共计算出526个数据集，以评估13个非线性HRV参数，并将其用于ANN的训练和测试。使用这些参数作为输入，使用ANN架构（13：7：1）在0.01的学习率下获得86.3％的分类精度。虽然使用SVM可以观察到90.5％的相对较高的准确度，以区分糖尿病和对照组。获得的结果表明，非线性HRV参数显示出由于糖尿病引起的明显变化，因此，与机器学习工具一起，这些可用于开发使用机器学习技术预测糖尿病的无创低成本实时预后系统。