计算机与现代化Issue(10)：44-50,7.DOI:10.3969/j.issn.1006-2475.2025.10.008
FA-CGRNet:无创高血糖预测的分类模型
FA-CGRNet:Non-invasive Classification Model for Hyperglycemia Prediction
摘要
Abstract
Current blood glucose detection methods are often invasive,causing inconvenience and potential risks.A non-invasive method using wearable devices to collect physiological data and user-input dietary information for real-time high blood glucose prediction is proposed.To improve prediction accuracy,a deep learning-based time series classification model,FA-CGRNet,is developed.Physiological data are preprocessed through denoising and resampling.Statistical features are extracted.A residual convolutional network is designed to extract and fuse features through convolution and residual connections.A feature enhancement module is utilized to calculate feature weights and perform feature selection.Finally,an LSTM model is employed to extract long-term dependency features from time series data.The model is tested on a public dataset from the BIG IDEAs Lab at Duke University.In the field of non-invasive blood glucose detection,the feature extraction method and network model pre-sented in this study demonstrated superior performance compared to existing time series classification models.The model's abil-ity to distinguish between positive and negative cases is significantly enhanced.Notably,the weighted F1 score is improved by over 6.2%,while the AUC is increased by more than 2.5%.These results underscore the effectiveness of the proposed approach in advancing non-invasive blood glucose monitoring techniques.关键词
无创高血糖预测/残差卷积/长短期记忆网络/特征增强模块/神经网络Key words
non-invasive hyperglycemia prediction/residual convolutional/LSTM/feature enhancement module/neural network分类
计算机与自动化引用本文复制引用
王蕾,赵康,殷秀强..FA-CGRNet:无创高血糖预测的分类模型[J].计算机与现代化,2025,(10):44-50,7.基金项目
国家自然科学基金资助项目(62261001) （62261001）
江西省核地学数据科学与系统工程技术研究中心基金资助项目(JELRGBDT202202) （JELRGBDT202202）
江西省放射性地学大数据技术工程实验室开放基金资助项目(JELRGBDT202103) （JELRGBDT202103）
