Diabetes Database Research Articles

PSO-FCM based data mining model to predict diabetic disease

Background and ObjectiveDiabetic disease is typically composed because of higher than normal blood sugar levels. Instead the production of insulin may be regarded insufficient. It has been noted in recent days that the percentage of diabetes-affected patients have grown to a larger extent throughout the world. Evidently, this problem must be taken more seriously in the coming days to ensure that the average percentages of diabetes-affected individuals are reduced. Recently, several research teams conducted detailed research on the data mining platform to determine the precision of each other. Data mining can be used by parametric modeling from the health data, including diabetic patient data sets, to synthesize expertise in the field. MethodsIn this study, a new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The combined Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM) are used to evaluate a set of medical data relating to a diabetes diagnosis challenge. ResultsExperiments are performed on the Pima Indians Diabetes Database. The sensitivity, specificity and accuracy metrics widely used in medical studies have been used to assess the effectiveness of the proposed system reliability. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods. ConclusionThe conclusion produced by using the method shows that, as compared with other models, the proposed PSO-FCM method delivers greater performance.

A deep learning approach based on convolutional LSTM for detecting diabetes

Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified and untreated, then it could be very deadliest. One can lead a healthy life with proper treatment if the presence of diabetes can be detected at an early stage. When the conventional process of detecting diabetes is tedious, there is a need of an automated system for identifying diabetes from the clinical and physical data. In this study, we developed a novel diabetes classifying model based on Convolutional Long Short-term Memory (Conv-LSTM) that was not applied yet in this regard. We applied another three popular models such as Convolutional Neural Network (CNN), Traditional LSTM (T-LSTM), and CNN-LSTM and compared the performance with our developed model over the Pima Indians Diabetes Database (PIDD). Significant features were extracted from the dataset using Boruta algorithm that returned glucose, BMI, insulin, blood pressure, and age as important features for classifying diabetes patients more accurately. We performed hyperparameter optimization using Grid Search algorithm in order to find the optimal parameters for the applied models. Initial experiment by splitting the dataset into separate training and testing sets, the Conv-LSTM-based model classified the diabetes patients with the highest accuracy of 91.38 %. In later, using cross-validation technique the Conv-LSTM model achieved the highest accuracy of 97.26 % and outperformed the other three models along with the state-of-the-art models.

P0775A RETROSPECTIVE OBSERVATIONAL COHORT STUDY TO ASSESS THE PREVALENCE OF CHRONIC KIDNEY DISEASE AMONG TYPE 2 DIABETES MELLITUS PATIENTS IN ONTARIO, CANADA

Abstract Background and Aims The CREDENCE trial with canagliflozin demonstrated definitive evidence of renal benefits to slow the progression of end stage renal disease (ESRD) in type 2 diabetes (T2D) patients with chronic kidney disease (CKD). This real-world study was undertaken to better understand the prevalence of CKD among T2D patients in Ontario using the CREDENCE trial criteria. Ontario is the largest province, accounting for 38.8% of Canada’s population in 20191. Patients were identified in the following cohorts: T2D-CKD, T2D-CKD+cardiovascular disease (CVD), T2D-CKD+stroke, and T2D-CKD+CVD or stroke. Method This population-based retrospective cohort study was conducted in partnership with IQVIA and the Institute for Clinical Evaluative Sciences (ICES). The ICES data repository contains publicly funded administrative health service records for the Ontario population eligible for universal health coverage since 1986. Patients’ eligibility for this study was aligned with enrolment criteria in the CREDENCE trial. Patients were ≥30 years of age at index and were identified as having both T2D and CKD. Diabetes patients were identified using the validated Ontario Diabetes Database (ODD), patients <19 years of age when first diagnosed with diabetes were excluded due to suspected type 1 diabetes (T1D). Additionally, patients with a T1D diagnosis at any time-point were excluded. CKD patients were identified through diagnosis/billing codes or estimated glomerular filtration rate(eGFR)<90 ml/min/1.73ml2 derived from serum creatinine laboratory values. Diagnosis/billing codes are expected to have poor sensitivity2,3 when used as the sole method to identify CKD. To account for anticipated missing information in each dataset, the capture-recapture method was used to obtain a more accurate estimate for the total prevalence. Capture-recapture accounts for incomplete ascertainment of administrative datasets by using the overlap between the datasets to derive an estimate of the total population4,5 (Figure 1). This method was used in Manitoba to estimate the total CKD population, using administrative and laboratory datasets4. Therefore, each cohort (T2D-CKD, T2D-CKD+CVD, T2D-CKD+stroke, and T2D-CKD+CVD or stroke) has utilized each of the following four methods to identify CKD patients: diagnosis/billing codes, eGFR, diagnosis/billing codes or eGFR, and capture-recapture method. Yearly point prevalence of CKD among T2D patients is reported for the five fiscal years (FY) between 2011/12 and 2015/16. Results The prevalence of T2D patients ≥30 years of age in Ontario has increased from 959,850 in FY2011/12 to 1,169,759 in FY2015/16 (Figure 2). The prevalence of CKD among T2D patients ≥30 years of age in Ontario has increased across all methods from FY2011/12 to FY2015/16: from 21% to 28% based on diagnosis/billing codes, 47% to 63% based on eGFR, 55% to 70% based on diagnosis/billing codes or eGFR, and 76% to 84% based on capture-recapture. Similarly, prevalence of T2D-CKD+CVD, T2D-CKD+stroke, and T2D-CKD+CVD or stroke has increased in most cases (Figure 3). Conclusion CKD is a common comorbidity amongst T2D patients ≥30 years of age. The study provides estimates of the prevalence of CKD in four cohorts of T2D patients with defined co-morbidities and shows that the use of diagnosis/billing codes alone may underestimate the prevalence of CKD in T2D patients. Furthermore, this real-world analysis highlights a significant, increasing prevalence of CKD among T2D patients ≥30 years of age in Ontario with all methods. On-going research aims to assess the burden of illness of patients with both T2D and CKD who are incident to T2D-related outcomes (CKD or CVD related death, kidney transplant, kidney dialysis, doubling of serum creatinine).

Detection of Diabetes By Machine Learning Technique

Diabetes is a most important health dispute that has reached distressing levels; today approximately half a billion individuals are living with diabetes universal. Diabetes is a state that damages the body’s capability to process glucose in blood, otherwise known as blood sugar. It is a metabolic disease that reasons high blood sugar. The hormone insulin transfers sugar from the blood into your cells to be stored for energy. With diabetes, your body either doesn’t make sufficient insulin or can’t efficiently use the insulin it does makes. The motive of this research is to design a method or prototype which can detect or predict the diabetes in patients with high precision. Therefore different machine learning classification algorithms namely decision tree, support vector machine, Naïve Bayes and k-NN are used in this research work for prediction of the diabetes. Two databases are used for experimentation. The first one is created from hospital with 82 patients and second one is readily available Pima Indian Diabetes database. The performances of different machine learning algorithms are estimated on different measures like Precision, Recall, F-measure and accuracy. The objective of this research is to study the accuracy of different machine learning algorithms and hence identify set of suitable algorithms for prediction of diabetes for further research work.

Diabetes Prediction and Analysis using Machine Learning Methods

Different computational procedures and gadgets are open for data examination. At the present time, took the advantages of those open developments to improve the adequacy of the estimate model for the desire for a Type-2 Diabetic Patient. We intend to inquire about how diabetes scenes are impacted by patients' characteristics and estimations. The capable gauge model is required for clinical researchers. Until generally, Type II diabetes was evaluated uncommon in children. The contamination is, nonetheless, creating among youths in peoples with high paces of Type II diabetes in adults. This work presents the adequacy of Gradient Boosted Classifier which is obscure in past current works. It is related to two AI figuring’s, for instance, Neural Networks, Random Forest. These estimations are applied to the Pima Indians Diabetes Database (PIDD) which is sourced from the UCI AI storage facility. The models made are surveyed by standard techniques, for instance, AUC, Recall, and Accuracy. As obvious, Gradient helped classifier clobbers other two classifiers in all introduction qualities.

A province wide review of transition practices for young adult patients with type 1 diabetes.

Many studies on the transition from paediatric to adult care focus on practices within a single institution or program. We examine the transition for young adults with type 1 diabetes across an entire Canadian province with a small, mostly rural population and high rates of type 1 diabetes: Newfoundland and Labrador (NL). Our aim is to determine how transition is occurring across the jurisdiction and identify methods for improving clinical services for paediatric patients with a chronic condition during their move into adult care. A provincial diabetes database and hospital admission data were reviewed for a cohort of young adults with type 1 diabetes who transitioned into adult care. Semi-structured interviews were conducted with paediatric and adult diabetes providers. Between 2008 and 2013, 93 patients with type 1 diabetes transitioned into adult care. Rates of diabetes-related hospitalizations increased from 15.6/100 person-years in the 3 years before their 18th birthday to 16.7/100 person-years in the three-year period after. Between 2017 and 2019, 15 interviews were conducted across the province's four regional health authorities. Various models of transition care are being employed, reflecting staff and resource availability in different centres. While no formal transition program was identified in either region, some providers, particularly in rural areas, reported being comfortable with their current transition practices. Suggested improvements included more structured processes, shared educational resources, expanding the role played by primary care physicians, and a dedicated transfer clinic. We found different approaches for transitioning patients with diabetes into adult care across NL. Yet this variation may not negatively impact patient outcomes, particularly in rural areas. The approach we employed of combining reviews of administration data with a detailed analysis of current processes could be employed in other jurisdictions to identify appropriate quality improvement initiatives.

Diabetes Prediction and Analysis using Machine Learning Methods

Different computational procedures and gadgets are open for data examination. At the present time, took the advantages of those open developments to improve the adequacy of the estimate model for the desire for a Type-2 Diabetic Patient. We intend to inquire about how diabetes scenes are impacted by patients' characteristics and estimations. The capable gauge model is required for clinical researchers. Until generally, Type II diabetes was evaluated uncommon in children. The contamination is, nonetheless, creating among youths in peoples with high paces of Type II diabetes in adults. This work presents the adequacy of Gradient Boosted Classifier which is obscure in past current works. It is related to two AI figuring’s, for instance, Neural Networks, Random Forest. These estimations are applied to the Pima Indians Diabetes Database (PIDD) which is sourced from the UCI AI storage facility. The models made are surveyed by standard techniques, for instance, AUC, Recall, and Accuracy. As obvious, Gradient helped classifier clobbers other two classifiers in all introduction qualities.

Cohort profile: the Funen Diabetes Database—a population-based cohort of patients with diabetes in Denmark

PurposeDetailed population-based data are essential to understanding the epidemiology of diabetes and its clinical course. This article describes the Funen Diabetes Database (FDDB). The purpose of the FDDB was to...

Prevalence and correlation of glycemic control achievement in patients with type 2 diabetes in Iraq: A retrospective analysis of a tertiary care database over a 9-year period

BackgroundThis study was designed to assess the achievement of a glycated hemoglobin (HbA1c) target in Iraqi type 2 diabetes mellitus (T2DM) patients via retrospective analysis of a tertiary care database over a 9-year period. MethodsA total of 12,869 patients with T2DM with mean (SEM) age: 51.4(0.1) years, and 54.4% were females registered into Faiha Specialized Diabetes, Endocrine and Metabolism Center(FDEMC) database between August 2008 and July 2017 were included in this retrospective study. Data were recorded for each patient during routine follow-up visits performed at the center every 3–12 months. ResultsPatients were under oral antidiabetic drugs (OAD; 45.8%) or insulin+ OAD (54.2%) therapy. Hypertension was evident in 42.0% of patients, while dyslipidemia was noted in 70.5%. Glycemic control (HbA1c <7%) was achieved by 13.8% of patients. Multivariate analysis revealed <55 years of age, female gender, >3 years duration of diabetes, HbA1c >10% at the first visit, presence of dyslipidemia, and insulin treatment as significant determinants of an increased risk of poor glycemic control. BMI <25 kg/m2 and presence of hypertension were associated with a decreased risk of poor glycemic control. ConclusionUsing data from the largest cohort of T2DM patients from Iraq to date, this tertiary care database analysis over a 9-year period indicated poor glycemic control. Younger patient age, female gender, longer disease duration, initially high HbA1c levels, dyslipidemia, insulin treatment, overweight and obesity, and lack of hypertension were associated with an increased risk of poor glycemic control in Iraqi T2DM patients.

Diagnosis of Type-2 Diabetes using Classification and Mining Techniques

Around two hundred and fifty million individuals, with a major part of them being ladies influenced by diabetes. This number may ascend to 380 million by another decade. The sickness has been named as the fifth deadliest illness in the world with not a single inevitable fix to be seen. With the ascent of data innovation and proceeding with an approach into the restorative and medicinal services part, the instances of diabetes and their side effects all around are archived. Information mining is a buzz word separating concealed data from an enormous arrangement of database. It assists scientists in building large database in the area of biomedical engineering. The Pima Indian diabetes database was used for investigation purpose. In this paper an attempt has been made to study the effect of various classification and mining Techniques like Decision Tree, Naïve Bayes, SVM, Regression etc on the diagnosis of Type-2 diabetes.

Higher Ultraviolet Radiation During Early Life Reduces Risk of Childhood Type 1 Diabetes Among Boys

Background: Ecological studies show an inverse association between type 1 diabetes incidence and ambient UV radiation (UVR) levels. We used large linked datasets to test ambient UVR during early life against type 1 diabetes risk at the individual level. Methods: We conducted a nested case-control study using linked data from state-wide administrative datasets. Cases (n=1819) were all children born in Western Australia from 1980-2014 with a diagnosis of type 1 diabetes on the Western Australian Children’s Diabetes Database between 0-16 years of age. Controls (n=27 259) were randomly selected from all live births in Western Australia and matched to cases on sex and date of birth. Daily UVR data from NASA satellites, that were date-and location-specific for each individual, were used to estimate total UVR dose for each trimester of pregnancy and the first year of life. Findings: Conditional logistic regression showed that type 1 diabetes risk was 44% lower in boys of mothers with UVR levels in the highest quartile (compared to the lowest quartile) during their third trimester of pregnancy (p=0·04). Higher UVR in the first year of life was also associated with a significantly lower risk of type 1 diabetes in later childhood among boys. Among girls, there was no evidence of associations between total UVR dose and type 1 diabetes risk. Interpretations: Higher UVR in the third trimester and first year of life appears to interact with sex-specific factors to lower type 1 diabetes risk among boys (but not girls) in Western Australia. Funding Statement: This research was supported by an Australian Government Research Training Program Scholarship and a PhD Top-Up scholarship from the Children’s Diabetes Centre, Telethon Kids Institute, Perth WA, Australia. The funding sources had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had access to all the data and all authors agreed on the decision to submit for publication. Declaration of Interests: The authors have no conflicts of interest to disclose. Ethics Approval Statement: Use of these data was approved by the Western Australian Department of Health (2016/05) and the University of Western Australia Human Research Ethics Committee.

Is type 3c diabetes the ‘middle child' of diabetes groups? Under appreciated, complicated and ignored.

Introduction: Patients with pancreatic disease commonly develop endocrine impairment and pancreatogenic diabetes(type 3c). However, precise definition and diagnostics are not yet clearly defined. Patients with type3c diabetes behave differently and have different healthcare requirements than those with types1&2 diabetes. Correct identification and classification is therefore crucial to ensure good management and outcomes. Method: Following ethical approval, a Diabetes Database (DD) maintained by the Department of Endocrinology and Department of Surgery Pancreatic Database (PD) in atertiary referral university hospital, were interrogated between the years 2004-2017. Both were cross-matched for common patients. Data on demographics, management, and complications were recorded. Results: There were 11,216 patients on the DD and 3,865 patients on the PD. Of these, n=327 cases were deemed to have pancretogenic diabetes. A minority (23.5%)were correctly labelled as type3c diabetes,while 63.9% classified as type 2 and 12.3% as type 1. Pancreatogenic diabetes patients were predominatly male (69%), with a mean age of 55 years (range:18-98 years), and a mean BMI of 29 37% had chronic pancreatitis, while 32% had acute pancreatitis and 31% had pancreatic cancer. Almost half (45%) were insulin-dependant, with 55% on oral hypoglycaemic agents. More than half (51%) had confirmed exocrine insufficiency, although faecal-elastase-1 was infrequently reported (12%). Fewer (41%) took enzyme replacement therapy. Frequent hypoglycaemiaoccurred in 17%. The majority of patients (54%) had at least one diabetic complication, while 10% had >3 complications. Conclusion: Type 3c diabetes is poorly recognised in patients with pancreatic diseases, increasing the risk of mismanagement. Hypo- and hyper-glycaemia, compounded by malabsorption, malnutrition, and frequent diabetic complications result in a difficult-to-manage group. Appropriate and timely diagnosis is crucial to ensure optimal outcomes.

Integrative analyses of biomarkers and pathways for adipose tissue after bariatric surgery

ABSTRACT We explored potential biomarkers and molecular mechanisms regarding multiple benefits after bariatric surgery. Differentially expressed genes (DEGs) for subcutaneous adipose tissue (AT) after bariatric surgery were identified by analyzing two expression profiles from the GEO. Subsequently, enrichment analysis, GSEA, PPI network, and gene-microRNAs and gene-TFs networks were interrogated to identify hub genes and associated pathways. Co-expressed DEGs included one that was up-regulated and 22 that were down-regulated genes. The enrichment analyses indicated that down-regulated DEGs were significantly involved in inflammatory responses. GSEA provided comprehensive evidence that most genes enriched in pro-inflammation pathways, while gene-sets after surgery enriched in metabolism. We identified nine hub genes in the PPI network, most of which were validated as highly expressed and hypomethylated in obesity by Attie Lab Diabetes and DiseaseMeth databases, respectively. DGIdb was also applied to predict potential therapeutic agents that might reverse abnormally high hub gene expression. Bariatric surgery induces a significant shift from an obese pro-inflammatory state to an anti-inflammatory state, with improvement in adipocyte metabolic function – representing key mechanisms whereby AT function improves after bariatric surgery. Our study deepens a mechanistic understanding of the benefits of bariatric surgery and provides potential biomarkers or treatment targets for further research.

Prevalence, incidence and outcomes of diabetes in Ontario First Nations children: a longitudinal population-based cohort study.

First Nations people are known to have a higher risk of childhood-onset type 2 diabetes, yet population-level data about diabetes in First Nations children are unavailable. In a partnership between Chiefs of Ontario and academic researchers, we describe the epidemiologic features and outcomes of diabetes in First Nations children in Ontario. We created annual cohorts from 1995/96 to 2014/15 using data from the Registered Persons Database linked with the federal Indian Register. We used the Ontario Diabetes Database to identify children with all types of diabetes and calculated the prevalence and incidence for First Nations children and other children in Ontario. We describe glycemic control in First Nations children and other children in 2014. In 2014/15, there were 254 First Nations children and 10 144 other children with diagnosed diabetes in Ontario. From 1995/96 to 2014/15, the prevalence increased from 0.17 to 0.57 per 100 children, and the annual incidence increased from 37 to 94 per 100 000 per year among First Nations children. In 2014/15, the prevalence of diabetes was 0.62/100 among First Nations girls and 0.36/100 among other girls. The mean glycosylated hemoglobin level among First Nations children was 9.1% (standard deviation 2.7%) and for other children, 8.5% (standard deviation 2.1%). First Nations children have substantially higher rates of diabetes than non-Aboriginal children in Ontario; this is likely driven by an increased incidence of type 2 diabetes and increased risk for diabetes among First Nations girls. There is an urgent need for strategies to address modifiable factors associated with the risk of diabetes, improve access to culturally sensitive diabetes care and improve outcomes for First Nations children.

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Over 30 million people in India are suffering from diabetes and many others are under the risk. Thus, early diagnosis and treatment is required to prevent diabetes and its associated health problems. This study aims to assess the risk of diabetes among individuals based on their lifestyle and family background. The risk of Type 2 diabetes was predicted using different machine learning algorithms as these algorithms are highly accurate which is very much required in the health profession. Once the model will be trained with good accuracy, then individuals can self-assess the risk of diabetes. In order to conduct the experiment, 952 instances have been collected through an online and offline questionnaire including 18 questions related to health, lifestyle and family background. The same algorithms were also applied to the Pima Indian Diabetes database. The performance of Random Forest Classifier is found to be most accurate for both datasets.

A robust voting approach for diabetes prediction using traditional machine learning techniques

The noteworthy advances in biotechnology and biomedical sciences have prompted a huge creation of information, for example, high throughput genetic information and clinical data, produced from extensive Electronic Health Records. To this end, utilization of machine learning and data mining techniques in biosciences is by and by crucial and fundamental in endeavors to change cleverly all accessible data into profitable knowledge. Diabetes mellitus is characterized as a gathering of metabolic issue applying critical weight on human health around the world. Broad research in all parts of diabetes (determination, pathophysiology, treatment, and so forth.) has prompted the age of tremendous measures of information. The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. The main theme of this work is to provide a system which can prognosticate the diabetes in patients with better accuracy. Here, eleven well-known machine-learning algorithms like Naive Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection of diabetes at an early stage. The evaluations of all the eleven algorithms are examined on various parameters like accuracy, precision, F-measure and recall. After cross-validation and hyper-tuning, the best three machine-learning algorithms are determined and then used in Ensemble Voting Classifier. The experimental results affirm that the pointed framework can accomplish to outstanding outcome of almost 86% accuracy of the Pima Indians Diabetes Database.

Diabetic retinopathy detection using red lesion localization and convolutional neural networks

Detecting the early signs of diabetic retinopathy (DR) is essential, as timely treatment might reduce or even prevent vision loss. Moreover, automatically localizing the regions of the retinal image that might contain lesions can favorably assist specialists in the task of detection.In this study, we designed a lesion localization model using a deep network patch-based approach. Our goal was to reduce the complexity of the model while improving its performance. For this purpose, we designed an efficient procedure (including two convolutional neural network models) for selecting the training patches, such that the challenging examples would be given special attention during the training process. Using the labeling of the region, a DR decision can be given to the initial image, without the need for special training.The model is trained on the Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1) database and is tested on several databases (including Messidor) without any further adaptation. It reaches an area under the receiver operating characteristic curve of 0.912−95%CI(0.897−0.928) for DR screening, and a sensitivity of 0.940−95%CI(0.921−0.959). These values are competitive with other state-of-the-art approaches.

Effects of exercise therapy on type 2 diabetes patients in the working mode of " combination of physical and health"

Objective To evaluate the effect of aerobic exercise combined with resistance exercise on blood sugar and constitution in patients with type 2 diabetes mellitus. Methods Fifty eligible subjects in community type 2 diabetes database were randomly selected and a model intervention group of 1+ 2+ 1 was established.The regular exercise intervention was conducted for six months by combining aerobic exercise with resistance exercise.The fasting blood sugar, blood pressure, heart rate, body mass and fat mass before and after intervention were compared by paired t test.Changes in related physical indicators. Results After 6 month of exercise therapy, fasting blood glucose of subjects reduced from (8.58±4.40) mmol/L to (6.29±1.72) mmol/L(P=0.032). Body weight also reduced from (62.44±7.35) Kg to (60.70±7.54) Kg(P=0.008). In physical quality related indexes, fat mass decreased significantly, while protein content increased significantly.There were no changes in muscle mass and fat free body weight.In addition, grip strength and flexion range of subjects significantly increased after exercise therapy.Comprehensive score of physical quality of subjects increased from (73.25±5.65) to (75.48±5.04)(P=0.010). Conclusion Exercise therapy of aerobic exercise combined with resistant exercise can effectively reduce blood glucose, body weight and fat mass, increase muscle strength, flexibility and physical quality.This also reflects that the community-led health and physical integration mode of work is an effective means to manage chronic diseases and enhance the physical fitness of residents. Key words: Combination of physical and health; Aerobic exercise; Resistant exercise; Type 2 diabetes

Performance Analysis of Big data Classification Techniques on Diabetes Prediction

Big data is extremely huge data sets analyzed computationally to expose patterns, trends, and prediction in order to make simpler the decision making. Predicting diseases became very important, it can be obtained with a large dataset using classification techniques. Various big data analytics tools are available for classification. Classification is the general technique used in the medical analysis for data prediction. In these paper classification algorithms like Support vector machine, Naïve Bayesian and C4.5 are discussed. The Pima Indian Diabetes Database (PIDD) is used in the analysis of the Classification algorithms to sort out and classify the people with diabetes positive and with diabetes negative it is openly accessible machine learning database found at UCI. The objective is to find the best suitable technique for prediction. Here, we used the comparison method with the results of three supervised learning algorithms based on three criteria, computation time taken, accuracy rate and error rate using the Tanagra tool. The classification algorithms are used to predict diabetes based on the data given. Accordingly, many classification techniques are there, from this study a few classification techniques suggested to be used in big data analysis, which has the probability to significantly progress the prediction. . As a result, a representative confusion matrix is displayed for making the verification process faster. From the results, it is concluded that C4.5 algorithm is best suited for predicting diabetes disease and also can be used in other disciplinary for making better prediction.

Decreasing Trends in Mean HbA1c Are Not Associated With Increasing Rates of Severe Hypoglycemia in Children: A Longitudinal Analysis of Two Contemporary Population-Based Pediatric Type 1 Diabetes Registries From Australia and Germany/Austria Between 1995 and 2016.

To investigate temporal trends in glycemic control and severe hypoglycemia rates for pediatric patients with type 1 diabetes from 1995 to 2016 by analyzing data from the longitudinal, prospective, population-based German/Austrian (Diabetes Patient History Documentation [DPV]) and Western Australian (Western Australian Children's Diabetes Database [WACDD]) diabetes registries. Patients diagnosed with type 1 diabetes aged <15 years were identified from the DPV (N = 59,883) and WACDD (N = 2,595) registries and data extracted for all clinic visits occurring between 1995 and 2016, inclusive. Mean HbA1c and severe hypoglycemia (self-reported loss of consciousness/convulsion) rates were calculated per 100 patient-years. Between 1995 and 2016, the annual mean HbA1c decreased from 8.3 to 7.8% in the DPV cohort and from 9.2 to 8.3% in the WACDD cohort. Over the same period, the severe hypoglycemia rate decreased by an annual average of 2% (relative risk 0.983 [95% CI 0.981, 0.986]) in the DPV cohort and 6% (relative risk 0.935 [95% CI 0.934, 0.937]) in the WACDD cohort. Concomitant decreasing trends in both HbA1c and severe hypoglycemia rates were observed in boys and girls, all age-groups, and injection therapy/pump regimen groups. Over the past two decades, there have been concurrent improvements in HbA1c and decreasing severe hypoglycemia rates in two contemporary, longitudinal, population-based pediatric cohorts of type 1 diabetes. Translation of these data into clinical practice and patient education may reduce fear of hypoglycemia and enable better glycemic control.