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Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network

Ben Ali J., Hamdi T., Fnaiech N., Di Costanzo V., Fnaiech F., Ginoux J. M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

828--840

EN

Recent technological advancements in diabetes technologies, such as Continuous Glucose Monitoring (CGM) systems, provide reliable sources to blood glucose data. Following its development, a new challenging area in the field of artificial intelligence has been opened and an accurate prediction method of blood glucose levels has been targeted by scientific researchers. This article proposes a new method based on Artificial Neural Networks (ANN) for blood glucose level prediction of Type 1 Diabetes (T1D) using only CGMdata as inputs. To show the efficiency of our method and to validate our ANN, real CGM data of 13 patients were investigated. The accuracy of the strategy is discussed based on some statistical criteria such as the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The obtained averages of RMSE are 6.43 mg/dL, 7.45 mg/dL, 8.13 mg/dL and 9.03 mg/dL for Prediction Horizon (PH) respectively 15 min, 30 min, 45 min and 60 min and the average of MAPE was 3.87% for PH = 15 min, knowing that the smaller is the RMSE and MAPE, the more accurate is the prediction. Experimental results show that the proposed ANN is accurate, adaptive, and very encouraging for a clinical implementation. Furthermore, while other studies have only focused on the prediction accuracy of blood glucose, this work aims to improve the quality of life of T1D patients by using only CGM data as inputs and by limiting human intervention.

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Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm

Hamdi T., Ben Ali J., Di Costanzo V., Fnaiech F., Moreau E., Ginoux J. M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

362--372

EN

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.

3

Prediction of Drape Coefficient by Artificial Neural Network

Ghith A., Hamdi T., Fayala F.

Autex Research Journal

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2015

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Vol. 15, no. 4

266--274

EN

An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

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A Principal Component Analysis (PCA) method for predicting the correlation between some fabric parameters and the drape

Hamdi T., Ghith A., Fayala F.

Autex Research Journal

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2014

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Vol. 14, no. 1

22--27

EN

A new method for predicting correlation between fabric parameters and the drape is developed. This method utilises a Principal Component Analysis (PCA) of intercorrelated influencing parameters (bending rigidity, weight, thickness) and the drape parameters (drape coefficient and the node number). This paper describes the PCA procedure and presents the similarities and contrasts between variables.