diabetes prediction


Diagnosis, prediction , and natural course of HIV-1 protease-inhibitor-associated lipodystrophy, hyperlipidaemia, and diabetes mellitus: acohort study
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The prevalence and severity of lipodystrophy syndrome with long- term therapy for HIV-1 infection that includes a protease inhibitor is unknown. We studied the natural course of the syndrome to develop diagnostic criteria and identifying markers that

The Melbourne Pre- Diabetes Study: prediction of type 1 diabetes mellitus using antibody and metabolic testing
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Objectives: To determine the utility of various autoantibodies in predicting progression to clinical diabetes in first-degree relatives of patients with type 1 diabetes mellitus. Participants: 3315 first-degree relatives of patients with type 1 diabetes (1161 parents, 1206

Prediction of diabetes by using artificial neural network
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This paper presents a study on the prediction of diabetes using different supervised learning algorithms of Artificial Neural Network. The network is trained using the data of 250 diabetes patients between 25 to 78 years old. The performance of each algorithm is further discussed

Diabetes Prediction with Supervised Learning Algorithms of Artificial Neural Network
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Diabetes is one of metabolic diseases where a patient has high blood sugar either caused by the body failure to produce enough insulin or the cells failure to respond to the produced insulin. Diabetes can be identified by studying on several readings taken from a patient such

Diabetes prediction using feature selection and classification
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Medical data mining is becoming increasingly important in healthcare. The diversity of medical data collected/stored for diagnosis and prognosis and the availability of widespread data mining techniques to process these data place medical data mining in a unique

Intelligent and effective diabetes risk prediction system using data mining
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Diabetes is not only a disease but also responsible for occurring different kinds of diseases such as heart attack, kidney disease, blindness and renal failure. With respect to Bangladesh, Diabetes is a deadly, disabling and cost disease whose risk is increasing at

Prediction of diabetes using soft computing techniques-A survey
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Neural Networks are one of the soft computing techniques that can be used to make predictions on medical data. Neural Networks are known as the Universal predictors. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood

Study of data mining algorithms for prediction and diagnosis of diabetes mellitus
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Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various available traditional methods for diagnosing diabetes are based on physical and chemical tests. These methods can have errors due to different uncertainties. A

Prediction of fetal macrosomia with ultrasound parameters and maternal glycemic controls in gestational diabetes mellitus
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Purpose of investigation: Evaluation of ultrasound measurements of fetal adipose subcutaneous tissue (ASCT), abdominal circumference (AC), liver length (LL), and amniotic fluid index (AFI) in prediction of fetal macrosomia (FM) and gestational diabetes mellitus

Improving the prediction rate of diabetes diagnosis using fuzzy, neural network, case based (FNC) approach
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In todays world the diabetes mellitus has become a major health problem among the people of all ages. Most of the researchers have proposed different systems to diagnose diabetes , in spite of which the accuracy of the prediction rate of diabetes is not so significant. Most of

Short-term diabetes blood glucose prediction based on blood glucose measurements
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Insulin Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amounts of insulin. Daily compensation of the deficiency requires 4-6 insulin injections to be taken daily, the aim of this insulin therapy

Utilization of data mining techniques for prediction of diabetes disease survivability
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Detection of knowledge patterns in clinical data through data mining. Data mining algorithms can be trained from past examples in clinical data and model the frequent times non-linear relationships between the independent and dependent variables. The consequential model

Prediction and prevention of type 2 diabetes
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Type 2 diabetes mellitus is one of the most costly and burdensome chronic diseases of our time and a condition that is increasing in epidemic proportions worldwide. Its complications are a significant cause of morbidity and mortality and a tremendous economic burden to the

Comparison a performance of data mining algorithms (CPDMA) in prediction of diabetes disease
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Detection of knowledge patterns in clinicial data through data mining. Data mining algorithms can be trained from past examples in clinic
al data and model the frequent times non-linear relationships between the independent and dependent variables. The

Modeling and prediction in diabetes physiology
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Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate

An Integrated Approach towards the prediction of Likelihood of Diabetes
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With the growth of Information and communication technologies, the health care industry is also producing extensively large data. For managing such large amount of data, an efficient knowledge discovery process is required. This field is developing fast and there is a big

Improving the prediction rate of diabetes using fuzzy expert system
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The use of fuzzy logic in disease diagnosis is very common and beneficial as it incorporates the knowledge and experience of physician into fuzzy sets and rules. Most of the research proposed different systems for the diabetes diagnosis. But their accuracy of prediction is not

Prediction on diabetes using data mining approach
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The main purpose of this paper is to predict how likely the people with different age groups are being affected by diabetes based on their life style activities and to find out factors responsible for the individual to be diabetic. Hence it is interesting to implement statistical

Prediction of the clinical remission using the C-peptide level in type 1 diabetes in children
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The aim of the study was to identify individual, clinical and metabolic factors that predict the C-peptide levels and appearance of clinical partial remission during the first year of type 1 diabetes (T1D) in children. Material and methods: 197 type 1 diabetic patients (84 female

Classification and Prediction of heart disease from diabetes patients using hybrid particle swarm optimization and library support vector machine algorithm
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The multi-factorial chronicle, severe disease among human is diabetes . As a result of abnormal level of glucose in body leads to heart attack, kidney disease, renal failure, Hyperglycemia and also cancer in organs like liver and pancreas. Many studies have been