A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks

2010 ◽  
Author(s):  
T. Jayalakshmi ◽  
A. Santhakumaran
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Method ◽  
Artificial Neural ◽  
Diagnosis Of Diabetes Mellitus

Artificial neural networks as a classification method in the behavioural sciences

1997 ◽  
Vol 40 (1) ◽  
pp. 35-43 ◽  
Author(s):  
David Reby ◽  
Sovan Lek ◽  
Ioannis Dimopoulos ◽  
Jean Joachim ◽  
Jacques Lauga ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Method ◽  
Artificial Neural ◽  
Behavioural Sciences

scholarly journals Application of artificial neural networks to detect bone remodeling changes in diabetes mellitus

2019 ◽  
Vol 2 (21) ◽  
pp. 43-46
Author(s):  
S. S. Safarova
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Bone Turnover ◽  
Fragility Fractures ◽  
Increased Risk ◽  
Artificial Neural ◽  
Risk Determinants

This paper describes the task of authentication of bone turnover indicators using the developed method of building a decision support system based on an artificial neural network. A method has been developed for the calculation of risk determinants, which helps the physician in early diagnosis to make an informed decision, based on the identification of changes in bone turnover that increased risk of fragility fractures in diabetes mellitus.

scholarly journals ARTIFICIAL INTELLIGENCE ON THE IDENTIFICATION OF DIABETES-RELATED OSTEOMETABOLIC DISORDERS

2021 ◽  
Vol 06 (01) ◽  
pp. 73-74
Author(s):  
Sain Safarova Sain Safarova
Keyword(s):  
Artificial Intelligence ◽  
Diabetes Mellitus ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Practice ◽  
Premature Death ◽  
Diabetic Patients ◽  
New Therapeutic Approaches ◽  
Patients With Diabetes ◽  
Artificial Neural

Introduction: Complications of diabetes mellitus (DM) are of great medical and social importance, as they cause severe disability and premature death of patients with diabetes mellitus. Bone remodeling disorders occurring in diabetes increase the risk of fractures and move the problem of diabetic osteopathy beyond the narrow specialty, making it the subject of extensive scientific research [1-3]. However, osteopathy remains an underestimated complication and is not considered in most diabetes guidelines. The fact that diabetic osteopathy is often asymptomatic leads to the fact that diabetic patients turn their attention to this pathology late and turn to a specialist, as a rule, already having a high degree of progression of this complication. One of the important issues is the timely detection and prediction of bone changes in diabetes mellitus. The introduction of artificial intelligence technologies (AIT) into clinical practice is one of the main trends in world medicine [4]. AIT and Artificial Neural Networks (ANN) can fundamentally change the criteria for diagnosis and prognosis, which will contribute to the development of new therapeutic approaches, improve the efficiency of medical care and reduce costs [5]. The prospects for using ANN can potentially provide almost limitless technical possibilities. Considering the possibilities of using these technologies in clinical practice, we came to the conclusion that the development and implementation of forecasting systems based on the construction of a model of an intelligent decision support system based on the apparatus of artificial neural networks is able to analyze clinical and laboratory indicators of patients with diabetes mellitus (DM) in order to predict the values of qualitative and quantitative indicators assessing the state of bone tissue.

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