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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 model for blood glucose prediction. The model 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...
The multi factorial, chronic, severe diseases like diabetes and cancer have complex relationship. When the glucose level of the body goes to abnormal level, it will lead to Blindness, Heart disease, Kidney failure and also Cancer. Epidemiological studies have proved that several cancer types are possible in patients having diabetes. Many researchers proposed methods to diagnose diabetes and cancer...
A method provides for the design of Fast Haar wavelet with the supervised neural network for diagnosis of diabetes mellitus with nearer accuracy with in short time application non-invasively is proposed. Our proposed work consists of two parts. First, analysis bank of haar wavelet is modified by using polyphase structure. Second, supervised back propagation neural network in the diagnosis of diabetes...
Medicine has always benefited from the technology. Artificial Neural Networks is currently the promising area of interest to solve medical problems. Diagnosis of diabetes is one of the most challenging problems in machine learning. This medical data set is seldom complete. Artificial neural networks require complete set of data for an accurate classification. The system explains how the pre-processing...
The aim of the present study is to design and develop a Decision Support System (DSS) closely coupled with an Electronic Medical Record (EMR), able to predict the risk of a Type 1 Diabetes Mellitus (T1DM) patient to develop retinopathy. The proposed system is able to store a wealth of information regarding the clinical state of the T1DM patient and continuously provide the health experts with predictions...
Hypoglycemia (low blood glucose) or the fear of hypoglycemia constitutes a significant barrier to the achievement of good glycemic control in the insulin treated diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia can be detected non-invasively. From a clinical study of six children with type 1 diabetes (T1D), associated with...
A series pair data of NIR spectral and measured BGL are collected for an OGTT experiment from a healthy volunteer. The collected data are then calibrated by using partial least squares (PLS) regression and feed-forward back-propagation neural network (NN). A comparative analysis between both calibration models is analysed. From the PLS and NN calibration models, root mean square error prediction of...
Diabetes is a serious disease during which the body's production and use of insulin is impaired, causing glucose concentration level to increase in the bloodstream. Regulating blood glucose levels as close to normal as possible, leads to a substantial decrease in long term complications of diabetes. In this paper, an intelligent neural network online optimal feedback treatment strategy based on nonlinear...
This paper documents the results of the research involving neural network-based blood glucose level forecasting systems for insulin-dependent diabetes patients. Forecast is made for continuous subcutaneous insulin injections and continuous subcutaneous glucose measurements. Elman, layer-recurrent, and NARX network architectures were considered in the research. The influence of the network architecture,...
One criticism of neural network controllers (neuro-controllers) is that the analytical model of the controller is not defined; therefore contemporary optimization techniques in control systems cannot be applied to the closed loop system. Often control parameters are tuned online because of inaccuracies due to linearity assumptions and reduction of order. This paper demonstrates how the specialized...
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