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| 基于迁移学习的近红外光谱非侵入性血糖检测研究 |
| Study on Non-invasive Blood Glucose Detection Using Near-Infrared Spectroscopy Based on Transfer Learning |
| 投稿时间：2025-04-16 修订日期：2025-04-16 |
| DOI： |
| 中文关键词: 迁移学习 近红外光谱 无创血糖检测 机器学习 特征选择 |
| 英文关键词: Transfer learning Near-infrared spectroscopy Non-invasive blood glucose detection Machine learning Feature selection |
| 基金项目: |
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| 中文摘要: |
| 目的：近红外光谱技术在无创血糖检测中面临因个体差异导致的模型泛化性不足问题，为了解决这一问题，提高数据利用率，并建立泛化能力更强的预测模型，本研究引入了迁移学习方法。方法：迁移学习是一种旨在将源域的知识迁移到目标域，从而提高目标域任务性能的机器学习技术。在本研究中，我们将社区人群数据作为源域，将学生群体数据作为目标域，以改善无创血糖检测模型在目标域上的表现。为了验证迁移学习的有效性，本研究对比了迁移学习前后模型的性能，结果：通过迁移学习策略，模型在无创血糖检测任务中的表现得到了显著提升，迁移后的模型MAPE与MAE分别下降了52.5460%与6.0805%，RMSE与MSE分别下降了10.7215%与12.1135%。结论：本研究展示了迁移学习在非侵入性血糖检测领域的应用前景，通过将源域中难以获取但与血糖值相关的特征迁移到目标域，有望在未来实现便携式、连续性的血糖监测，这将极大地提高糖尿病患者的生活质量。无创血糖检测技术的进步，不仅能够减少患者的痛苦，还能提供更为便捷的血糖监测手段，为糖尿病管理提供有力支持。 |
| 英文摘要: |
| Objective: Near-infrared spectroscopy technology in noninvasive blood glucose testing faces the problem of insufficient model generalization due to individual differences, and in order to solve this problem, improve data utilization, and build predictive models with stronger generalization ability, this study introduces a transfer learning approach. Method: Migration learning is a machine learning technique that aims to improve task performance in the target domain by transferring knowledge from the source domain to the target domain. In this study, we used community population data as the source domain and student population data as the target domain to improve the performance of the noninvasive glucose detection model on the target domain. In order to verify the effectiveness of migration learning, this study compares the performance of the model before and after migration learning. Result: the model''s performance on the noninvasive glucose detection task is significantly improved by the migration learning strategy, and the MAPE and MAE of the migrated model decreases by 52.5460% and 6.0805%, respectively, and the RMSE and MSE decreases by 10.7215% and 12.1135%. Conclusions: This study demonstrates the promising application of transfer learning in the field of non-invasive blood glucose detection, which is expected to realize portable and continuous blood glucose monitoring in the future by migrating the features that are difficult to access in the source domain but are related to blood glucose values to the target domain, which will greatly improve the quality of life of diabetic patients. Advances in noninvasive glucose testing technology will not only reduce patients'' pain, but also provide a more convenient means of glucose monitoring, which will provide strong support for diabetes management. |
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