Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm
Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm
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DOI：
10.1016/j.bbe.2018.02.005
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发表时间：
2018-01-01
影响因子：
6.4
通讯作者：
Ginoux, Jean-Marc
中科院分区：
文献类型：
--
作者：
Hamdi, Takoua;Ben Ali, Jaouher;Ginoux, Jean-Marc
1型糖尿病（T1 D）是一种慢性疾病，需要患者了解他们的血糖值，以确保血糖水平尽可能接近正常。因此，预测血糖水平的能力是临床研究人员的极大兴趣。从这个意义上说，文献中有很多可以预测血糖水平的解决方案。不幸的是，这些方法需要患者指定他们的日常活动：膳食摄入，胰岛素注射和情绪因素，这可能容易出错。为了减轻专利的负担，这项工作提出仅使用连续葡萄糖监测（CGM）数据来预测血糖水平，而不受其他因素的影响。为了支持这一点，支持向量回归（SVR）和差分进化（DE）算法进行了研究。利用12例患者的真实的CGM数据对该方法进行了验证。在预测时间为15 min、30 min、45 min和60 min时，平均均方根误差（RMSE）分别为9.44mg/dL、10.78mg/dL、11.82mg/dL和12.95mg/dL。基于差分进化算法的支持向量回归机具有鲁棒性强、自动化程度高、不需要人工干预等优点，预测精度高。(C)2018波兰科学院纳莱茨生物控制学和生物医学工程研究所。Elsevier B. V.出版，保留所有权利。
Type 1 diabetes (T1D) is a chronic disease requiring patients to know their blood glucose values in order to ensure blood glucose levels as close to normal as possible. Hence, the ability to predict blood glucose levels is of a great interest for clinical researchers. In this sense, the literature is rich with several solutions that can predict blood glucose levels. Unfortunately, these methods require the patient to specific their daily activities: meal intake, insulin injection and emotional factors, which can be error prone. To reduce this burden on the patent, this work proposes to use only continuous glucose monitoring (CGM) data to predict blood glucose levels independently of other factors. To support this, support vector regression (SVR) and differential evolution (DE) algorithms were investigated. The proposed method is validated using real CGM data of 12 patients. The obtained average of root mean square error (RMSE) was 9.44,10.78,11.82 and 12.95 mg/dL for prediction horizon (PH) respectively equal to 15, 30, 45 and 60 min. The results of the present study and comparison with some previous works show that the proposed method holds promise. The SVR based on DE algorithm achieved high prediction accuracy while being robustness, automatic, and requiring no human intervention. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.