Blood glucose control is an essential goal for the patients who have Type‐1 diabetes (T1D). The prediction of the blood glucose levels for the next 30‐minute is crucial. If the predicted blood glucose level is in the critical ranges, and these predictions can be known in advance, then the patients can take the necessary cautions to prevent from it. In this article, we propose a modified fuzzy particle swarm optimization algorithm for the prediction of blood glucose levels of 30‐minute after the last measurement. We form the average and patient‐specific models to predict the blood glucose level of the patients. Both models are tested on two different datasets which contain patients with T1D. The experimental results are evaluated in terms of root mean squared error and Clarke error grid analysis metrics. The results indicate that our proposed modified algorithm is feasible to be applied to the prediction of blood glucose levels. In addition, this approach can assist patients with T1D for their blood glucose control.

中文翻译：

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基于改进的模糊时间序列和粒子群算法的糖尿病血糖水平预测

血糖控制是患有1型糖尿病（T1D）的患者的基本目标。预测接下来30分钟的血糖水平至关重要。如果预测的血糖水平在临界范围内，并且可以提前知道这些预测，则患者可以采取必要的预防措施来预防。在本文中，我们提出了一种改进的模糊粒子群优化算法，用于预测上次测量后30分钟的血糖水平。我们形成平均模型和特定于患者的模型，以预测患者的血糖水平。两种模型均在包含T1D患者的两个不同数据集上进行了测试。根据均方根误差和Clarke误差网格分析指标评估了实验结果。结果表明，我们提出的改进算法是可行的，可以应用于血糖水平的预测。此外，这种方法可以帮助患有T1D的患者控制血糖。