A nonlinear fuzzy linguistic prediction (NFLP) model for acute hyperglycemia prediction is proposed in this paper. The model used IF–THEN expressions which are human-readable and easy to understand. Using cardiac electrophysiological signals as the input, the model can predict actuation durations and concentrations of acute hyperglycemia. The prediction results are compared with the ones of four classical models which are partial least squares (PLS), least-square support vector machine (LSSVM), back-propagation neural network (BPNN) and Takagi–Sugeno (T–S) model. The results show that the proposed method has high prediction accuracy. The method can provide support for clinical diagnosis of acute hyperglycemia.

中文翻译：

---

基于心脏电生理信号的急性高血糖非线性模糊语言预测模型

本文提出了一种用于急性高血糖预测的非线性模糊语言预测（NFLP）模型。该模型使用了人类可读且易于理解的 IF-THEN 表达式。使用心脏电生理信号作为输入，该模型可以预测急性高血糖的驱动持续时间和浓度。将预测结果与偏最小二乘 (PLS)、最小二乘支持向量机 (LSSVM)、反向传播神经网络 (BPNN) 和 Takagi-Sugeno (T-S) 模型这四种经典模型的预测结果进行比较。结果表明，该方法具有较高的预测精度。该方法可为急性高血糖的临床诊断提供支持。