Comparison of algorithms for the prediction of glucose levels in patients with diabetes (original) (raw)
Related papers
Journal of Electrical and Computer Engineering, 2011
Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE 5 day ) not used during ANN training. For BGL predictions of up to 1 hour a RMSE 5 day of (±SD) 0.15 ± 0.04 mmol/L was observed. For BGL predictions up to 10 hours, a RMSE 5 day of (±SD) 0.14 ± 0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.
A Neural Network Approach In Predicting The Blood Glucose Level For Diabetic Patients
2009
Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.
Blood glucose prediction using neural network
Advanced Biomedical and Clinical Diagnostic Systems VI, 2008
Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network (NN) model for blood glucose prediction. It is a sequential one using a Long-Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by the Wilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.
Biocybernetics and Biomedical Engineering, 2020
Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model's features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.
Blood glucose prediction for diabetes therapy using a recurrent artificial neural network
9th European Signal Processing Conference (EUSIPCO 1998), 1998
Expert short-term management of diabetes through good glycaemic control, is necessary to delay or even prevent serious degenerative complications developing in the long term, due to consistently high blood glucose levels (BGLs). Good glycaemic control may be achieved by predicting a future BGL based on past BGLs and past and anticipated diet, exercise schedule and insulin regime (the latter for insulin dependent diabetics). This predicted BGL may then be used in a computerised management system to achieve short-term normoglycaemia. This paper investigates the use of a recurrent artificial neural network for predicting BGL, and presents preliminary results for two insulin dependent diabetic females.
Diabetes has become a major health challenge affecting nearly 300 million people around the world. Complications of diabetes can be prevented by proper monitoring and regulation of glucose concentration in blood plasma. Continuous Glucose Monitoring Systems help to track the time course of blood glucose. These devices have the additional feature of giving threshold alert and predictive alert which is needed for an early warning of impending hypoglycemia. However, the accuracy of predictive alerts in currently available CGM devices is not very promising. Various algorithms have been developed in this regard by researchers. Still, a 100% accuracy has not been achieved. In our work, we have approached this prediction by training a simple neural network with the extracted features of continuous glucose monitoring sensor data time series. The data was obtained in three different ways, one set from the Self Monitoring Blood Glucose values, the second set from a diabetes resource and the third one from the patients using continuous glucose monitoring systems. A feed forward neural network with back propagation algorithm is trained with features of input patterns. The network is trained and validated to meet out the performance goal. The Root Mean Square Error between the actual glucose value and the predicted glucose value is used as the performance measure. It is observed that as the length of prediction horizon extends, the error increases. However, tracking of Hypoglycemic and Hyperglycemic trends are superior to the earlier approaches.
2017
In this thesis, walk forward testing is used to evaluate the performance of two long short-term memory (LSTM) models for predicting blood glucose values for patients with type 1 diabetes. The models are compared with a support vector regression (SVR) model as well as with an auto regressive integrated moving average (ARIMA) model, both of which have been used in related research within the area. The best performing long short-term model produces results equal to those of the SVR model and it outperforms the ARIMA model for all prediction horizons. In contrast to models in related research, this LSTM model also has the ability to assign a level of confidence to each prediction, adding an edge in practical usability.
2020
In this paper, a comparison of two different approaches that can be used in developing time series mathematical models (MM) of diabetes mellitus was carried out. The trade-off should be considered between the complexity of the model and its accuracy to predict future glucose concentration. This work is a continuation of the author's work and results obtained previously and showed the potential and superiority of using autoregressive with exogenous terms (ARX) model in describing the dynamics of diabetes. Moreover, it is shown that despite the models are of general form but they are different depending on individuals' regimen of diabetes management. The last fact was demonstrated by using six diabetic patients' records, with rich information about their life style and treatment program, to derive models. In addition to that an answer is given to two main questions: how many future samples of glucose levels can be predicted with acceptable accuracy and what is the accep...
Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks
2020
The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention, preventing the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional...
An Improved Artificial Neural Network Model for Effective Diabetes Prediction
Complexity, 2021
Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of ne...