کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6484169 | 1416073 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm
ترجمه فارسی عنوان
پیش بینی دقیق قند خون پیوسته بر اساس رگرسیون بردار پشتیبانی و الگوریتم تکاملی دیفرانسیل
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
چکیده انگلیسی
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 12.95Â mg/dL for prediction horizon (PH) respectively equal to 15, 30, 45 and 60Â 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biocybernetics and Biomedical Engineering - Volume 38, Issue 2, 2018, Pages 362-372
Journal: Biocybernetics and Biomedical Engineering - Volume 38, Issue 2, 2018, Pages 362-372
نویسندگان
Takoua Hamdi, Jaouher Ben Ali, Véronique Di Costanzo, Farhat Fnaiech, Eric Moreau, Jean-Marc Ginoux,