Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features

Alfian, Ganjar; Syafrudin, Muhammad; Anshari, Muhammad; Beneš, Filip; Atmaji, Fransiskus Tatas Dwi; Fahrurrozi, Imam; Hidayatullah, Ahmad Fathan; Rhee, Jongtae

doi:10.1016/j.bbe.2020.10.004

Cited by 55 publications

(51 citation statements)

References 63 publications

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“…Therefore, we highlight some studies that use different sources for feature and contextual information integration. Features based on BG in the last 30 minutes is clear to have a PH of 30 and 60 minutes (Alfian et al, 2020;E I Georga et al, 2015;Eleni I. Georga et al, 2015). The contribution of other features, like meal, insulin or exercise, are lower but not insignificant (Eleni I. Georga et al, 2015).…”

Section: A Prediction Modelsmentioning

confidence: 99%

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Hypoglycaemia Prediction Models With Auto Explanation

et al. 2022

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World-wide statistics show a considerable growth of the occurrence of different types of Diabetes Mellitus, posing diverse challenges at many levels for public health policies. Some of these challenges may be addressed by means of computerised systems which may pave the way to provide practitioners with insight on their patient's conditions anywhere and at anytime, but also to empower Diabetes patients as managers of their health. These systems for disease management come in many shapes and sizes, being the most promising trends the ones that involve expert systems that comprise specialised knowledge, use predictive models, feature engineering and reasoning. This study presents the state-of-the-art on reasoning and prediction models related with either blood glucose level or hypoglycaemia events. The main findings revealed are that there is room for improvement on predictive models, namely to enhance its accuracy and ability to forecast future events into a wider time frame. On the other hand, reasoning models are understudied and its usage in Diabetes management is reduced. We discuss an architecture that combines a predictive model and a reasoning system, with the objective of alerting of impending occurrences and interpret the current situation to accurately advise the diabetic user.

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Section: A Prediction Modelsmentioning

confidence: 99%

“…For one patient the combination of features is the same for the two PH (CGM, insulin and reported meals). In Alfian et al (2020) the contribution of time domain features shows potentialities for predictions. Also, the use of time of day includes some novel features highly connected to glucose dynamics (Eleni I. Georga et al, 2015).…”

Section: A Prediction Modelsmentioning

confidence: 99%

Hypoglycaemia Prediction Models With Auto Explanation

et al. 2022

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“…Machine learning, deep learning and AI based approaches have been used for detection and classifications various diseases [15] , [16] , [17] . Thus, as an alternative, AI-based solutions can provide efficient solutions that can help in automatic learning of features/patterns from CT scan images, which can augment the capabilities of radiologists in better decision-making and more effective management of the situation.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of COVID-19 from CT scan using convolutional neural network

Mishra

Singh

Joshi

2021

Biocybernetics and Biomedical Engineering

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Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model’s diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

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“…So, the features of historical data can be extracted into the prediction methods for further improvement. Inspired by this thought, Alfian et al [13] developed the multilayer perceptron (MLP) with time-domain features, where time-domain features are expressed by statistical attributes like minimum, maximum, mean, and standard deviation. In addition to the thought on data features, some researchers tended to add multiple inputs into the predictive model for better performance [14][15][16], where the multiple inputs include insulin, carbohydrate, meal-derived glucose, and energy expenditure.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, with the development of the CGM device, researchers are devoted to establishing the effective BG prediction method for the help of diabetes management. One way of them is to design the model with more consideration of the feature of CGM data, such as the MLP with timedomain features [13]; the other way is to collect other inputs, such as the RNN implemented with LSTM [16]. But in real applications, other inputs are difficult to collect continuously and automatically without the action of the human [13].…”

Section: Introductionmentioning

confidence: 99%

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Xie

Yang

2021

Med Biol Eng Comput

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This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

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Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features

Cited by 55 publications

References 63 publications

Hypoglycaemia Prediction Models With Auto Explanation

Hypoglycaemia Prediction Models With Auto Explanation

Automated detection of COVID-19 from CT scan using convolutional neural network

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

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