Blood glucose prediction with deep neural networks using weighted decision level fusion

Dudukcu, Hatice Vildan; Taşkıran, Murat; Yıldırım, Tülay

doi:10.1016/j.bbe.2021.08.007

Cited by 29 publications

(18 citation statements)

References 25 publications

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“…Considering the nonlinearity and complexity of the BG series, this paper applies the optimized GRU by the improved bacterial foraging algorithm to the field of BG prediction [ 19 , 20 ]. The wrist was selected to acquire the pulse signals simultaneously, and body temperature series with minimally invasive extraction of BG signals from upper-arm-based subcutaneous interstitial fluid was selected to construct the training and test dataset [ 21 , 22 ]. Experimental results show that our proposed method has high accuracy and adaptability and is better than similar types of deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Zhang

Gao

2022

Journal of Healthcare Engineering

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Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

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Section: Introductionmentioning

confidence: 99%

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Zhang

Gao

2022

Journal of Healthcare Engineering

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“…Dudukcu et al (2021) predicted blood glucose by virtue of deep neural networks. LSTM, WaveNet, and gated recurrent units, and decision-level combinations of these architectures were deep learning methods which were used to predict blood glucose . Zhang et al (2021) adopted deep learning and regression approaches to forecast blood glucose levels for type 1 diabetes .…”

Section: Introductionmentioning

confidence: 54%

“…LSTM, WaveNet, and gated recurrent units, and decision-level combinations of these architectures were deep learning methods which were used to predict blood glucose. 35 Zhang et al (2021) adopted deep learning and regression approaches to forecast blood glucose levels for type 1 diabetes. 36 Four data-driven models including different neural network architectures, a reservoir computing model, and a linear regression approach were employed in their study.…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al (2021) employed fully CNN for prediction of arterial blood pressure waveforms from photoplethysmogram signals . Others attempted to carefully evaluate the applicability of ML in the estimation of blood glucose levels. − Ben Ali et al (2018) used ANN for continuous blood glucose level prediction of type 1 diabetes . D’Antoni et al (2020) constructed an auto-regressive time-delayed jump neural network for blood glucose level prediction .…”

Section: Introductionmentioning

confidence: 99%

“…RF, adaptive boosting, and extreme gradient boosting models for blood glucose prediction of type 1 diabetes 34. They compared results of predictive models and concluded that ANN outperforms other intelligence-based models Dudukcu et al (2021). predicted blood glucose by virtue of deep neural networks.…”

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confidence: 99%

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Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Alizamir

Gholami

Bahrami³

et al. 2022

ACS Omega

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Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques—optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)—were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model’s accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin’s RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

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Development of an Explainable Deep Learning-Based Decision Support System for Blood Glucose Levels Forecasting in Type 1 Diabetes Using Edge Computing

Longo,

D’Antoni,

Petrosino

et al. 2024

IFMBE Proceedings

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Blood glucose prediction with deep neural networks using weighted decision level fusion

Cited by 29 publications

References 25 publications

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Development of an Explainable Deep Learning-Based Decision Support System for Blood Glucose Levels Forecasting in Type 1 Diabetes Using Edge Computing

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