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Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm...
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...
Many real world problems can be solved with Artificial Neural Networks in the areas of pattern recognition, signal processing and medical diagnosis. Most of the medical data set is seldom complete. Artificial Neural Networks require complete set of data for an accurate classification. This paper dwells on the various missing value techniques to improve the classification accuracy. The proposed system...
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...
Diabetes mellitus is a chronic metabolic disease that displays hyperglycaemia and that is strongly linked to micro and macro-vascular complications and neuropathic ones. The World Health Organization (WHO) states that there are around 171 million diabetic patients in the world, it's also estimated that this amount will double by 2030. We have performed a preliminary study on 35 volunteers, including...
The present work is a classification problem applied to diagnosis of diabetes mellitus using back propagation algorithm of artificial neural network (ANN). The data base used for training and testing the ANN has been collected from Sikkim Manipal Institute of Medical Sciences Hospital, Gangtok, Sikkim for the diabetic patients of the state of Sikkim. This work is validated by comparing the network...
The present study describes the design of an Artificial Neural Network to synthesize the Approximation Function of a Pedometer for the Healthy Life Style Promotion. Experimentally, the approximation function is synthesized using three basic digital pedometers of low cost, these pedometers were calibrated with an advanced pedometer that calculates calories consumed and computes distance travelled with...
This study theoretically analyzes and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared....
Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years; from 115 million in 2000 to 284 million in 2030. In type I diabetes, the disease is caused by the failure of the pancreas to produce a sufficient amount of insulin which leads to an uncontrolled increase in blood glucose unless the...
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