Background and objective: Blood glucose levels in humans change over time. Continuous glucose monitoring system (CGMS), can constantly monitor the change of blood glucose concentration. Given the historical data of blood glucose, predicting the trend of blood glucose in a short term is important for diabetes. Appropriate behaviors can be adopted to prevent hypoglycemia or hyperglycemia. Methods: The method proposed in this paper only uses historical blood glucose data as input, rather than complex multi-dimensional input. Previous articles have demonstrated that canonical correlation analysis (CCA) can effectively predict blood glucose. The linear relationship between historical blood glucose values and predicted values was only considered regrettably. To compensate for this, this paper adds a kernel function to find out the non-linear relationship between blood glucose. In the introduced kernel function, some parameters need to be adjusted. To reduce the deviation caused by manual parameter adjustment, this paper discusses the role of particle swarm optimization (PSO). Besides, this article puts forward an error compensation for CCA to enhance the precision. Finally based on the prediction results of PSO-KCCA, a personalized hypoglycemic warning threshold is proposed. Results: The proposed method is validated using clinical data by the root mean square error (RMSE) and differential coefficient (R²). The average RMSE result in PSO-KCCA was 8.01, 11.98, 12.45, 13.23, 14.53, 16.40 mg/dL in prediction horizon (PH) $=$ 5, 10, 15, 20, 25, 30 min. The average R² was 0.95, 0.95, 0.98, 0.97, 0.98, and 0.97, respectively. The CCA with error compensation (EC-CCA) reduced RMSE by 33.45% compared with CCA. For the hypoglycemic warning, the average sensitivity obtained at 6 different PH values was 94.37%, and the specificity was 92.25%. Conclusions: The experimental results confirm the effectiveness of PSO-KCCA in blood glucose prediction. The proposed EC-CCA successfully reduces the delay in the time series prediction. The personalized hypoglycemic warning threshold consider the influence of the model accuracy on the prediction results. This method guarantees the rate of underreporting during monitoring and ensures patient safety.

背景与目的：人体血糖水平会随着时间而改变。连续血糖监测系统（CGMS），可以不断监测血糖浓度的变化。根据血糖的历史数据，短期内预测血糖趋势对糖尿病很重要。可以采取适当的行为来预防低血糖或高血糖症。方法：本文提出的方法仅使用历史血糖数据作为输入，而不是复杂的多维输入。先前的文章已证明规范相关分析（CCA）可以有效预测血糖。仅遗憾地考虑了历史血糖值和预测值之间的线性关系。为了弥补这一点，本文增加了一个核函数来找出血糖之间的非线性关系。在引入的内核功能中，需要调整一些参数。为了减少由手动参数调整引起的偏差，本文讨论了粒子群优化（PSO）的作用。此外，本文提出了对CCA的误差补偿，以提高精度。最后，根据PSO-KCCA的预测结果，结果：所提出的方法已通过临床数据的均方根误差（RMSE）和微分系数（R ²）验证。在预测范围（PH）中，PSO-KCCA的平均RMSE结果为8.01、11.98、12.45、13.23、14.53、16.40 mg / dL$=$5、10、15、20、25、30分钟。平均R ^2分别为0.95、0.95、0.98、0.97、0.98和0.97。与CCA相比，带错误补偿的CCA（EC-CCA）将RMSE降低了33.45％。对于降糖警告，在6个不同的PH值下获得的平均敏感性为94.37％，特异性为92.25％。结论：实验结果证实了PSO-KCCA在血糖预测中的有效性。提出的EC-CCA成功减少了时间序列预测中的延迟。个性化的降血糖警告阈值考虑了模型准确性对预测结果的影响。此方法可确保监视期间漏报率，并确保患者安全。