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Diabetes mellitus is one of the chronic disease asrecent estimation in 2015 shows 415 million people sufferingfrom diabetes worldwide and estimated to have deaths of 1.5 to 5 million each year. It is very important to forecast tool whichcan be used to determine whether someone has diabetes or not. There are some methods which produce accurate predictionand Artificial neural network using Back propagation...
This study proposes on the prediction and classification of Diabetes Mellitus using Artificial Neural Network (ANN) and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS). The network was trained by using the data of 100 individuals with mean age of 42 years with an equal proportion of male and female. The performance of each approach is further discussed on the basis of accuracy and validates accurate...
In 2010, Global Status Report on NCD World Health Organization (WHO) reported that 60 percent of deaths in the world caused by the non-communicable diseases, and one of the non-communicable diseases that consumed a lot of attention was diabetes mellitus. Diabetes is a serious threat to the health development, because diabetes is a disease that caused most other diseases (complications), such as blindness,...
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 fuzzy logic based image processing application has been developed here which noninvasively measures the blood sugar level of a person from his /her urine by noting the colour change in its reaction with Benedicts reagent and displays the result so that apart from the patient, others also get informed. This system helps a diabetic patient to regularly monitor and control his/her blood sugar level...
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...
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...
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