In the U.S., over a third of adults are pre-diabetic, with 80\% unaware of
their status. This underlines the need for better glucose monitoring to prevent
type 2 diabetes and related heart diseases. Existing wearable glucose monitors
are limited by the lack of models trained on small datasets, as collecting
extensive glucose data is often costly and impractical. Our study introduces a
novel machine learning method using modified recurrence plots in the frequency
domain to improve glucose level prediction accuracy from wearable device data,
even with limited datasets. This technique combines advanced signal processing
with machine learning to extract more meaningful features. We tested our method
against existing models using historical data, showing that our approach
surpasses the current 87\% accuracy benchmark in predicting real-time
interstitial glucose levels.

美国有超过三分之一的成年人是糖尿病前期患者，其中 80％不知晓自己的状况。为了预防 2 型糖尿病和相关心脏疾病，需要更好的血糖监测。现有的可穿戴式血糖监测仪在小型数据集训练方面存在局限性，因为收集大量的血糖数据通常代价高且不现实。我们的研究采用改进的频域改变重现图机器学习方法，即使在有限数据集下，也能提高从可穿戴设备数据中预测血糖水平的准确性。该技术将先进的信号处理与机器学习相结合，提取更有意义的特征。我们使用历史数据对我们的方法进行了测试，结果显示我们的方法在预测实时间质血糖水平方面超过了当前的 87％准确性基准。