Top PDF Data mining for the diagnosis of type 2 diabetes

Data mining for the diagnosis of type 2 diabetes

Data mining for the diagnosis of type 2 diabetes

Abstract-Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial meta plasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are supe­ rior to obtained by DT and Be.
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The Effect of Prostate Cancer Diagnosis on Adherence to Medications for Patients with Type 2 Diabetes

The Effect of Prostate Cancer Diagnosis on Adherence to Medications for Patients with Type 2 Diabetes

To date, few studies have explored whether being diagnosed with cancer interrupted medication adherence for their non-cancer chronic conditions.35-37 This study examined the effect of being diagnosed with prostate cancer on adherence to OHAs among patients with type 2 diabetes. Non-metastatic, lower-risk prostate cancer is an excellent case study for studying survivorship care because it is prevalent and has high survival rates; diabetes is important not only because of its prevalence but also its morbidity and associated health care costs. This study hypothesized that patients would become less adherent to their OHAs during the first year post – prostate cancer diagnosis compared to their pre-diagnosis adherence levels, however their adherence would return to similar rates during the second year after being diagnosed. This study found that patients diagnosed with prostate cancer did have decreased adherence when compared with controls in the period immediately following cancer-diagnosis; however, contrary to our hypothesis, adherence to their diabetes medications never returned to pre-diagnosis levels. A similar decline was observed among all stages of prostate cancer patients during the 6 months post-diagnosis.17 However, our current study had a longer follow-up period (2 years vs. 6 months post– cancer diagnosis) and more years of data. In addition, This study only included patients with non- metastatic prostate cancer, a group for whom managing comorbidities would be a higher priority than patients with more aggressive cancer. The finding that diabetes patients diagnosed with a highly survivable cancer have decreased adherence to their diabetes medications two years after diagnosis is concerning. Certainly, primary care physicians and oncologists should not necessarily assume that their patients with chronic diseases will return to their baseline medication adherence levels after completing cancer treatments, particularly when patients believe controlling their diabetes or other chronic conditions is not as necessary as it truly is. Given the morbidity and mortality from non-cancer chronic conditions, providers should actively emphasize the importance of taking all medications.
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Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining

Type 2 diabetes is a chronic disease and one of the most common endocrine diseases including 90 to 95 percent of diabetic patients (American Diabetes Association, 2013) with different degrees of prevalence in various societies (King, Aubert, & Herman, 1998). It was recognized by an asymptomatic phase between the real onset of diabetic hyperglycemia and clinical diagnosis which lasts at least for 4-7 years (Brown, Critchley, Bogowicz, Mayige, & Unwin, 2012). Late or lack of diabetes diagnosis causes the increase of various chronic vascular complications (Heydari, Radi, Razmjou, & Amiri, 2010). Moreover, early diagnosis and prevention of diabetes reduces the high expenses associated with disease control and complication treatments and prevents hospital admissions due to its severe complications (Karter et al., 2003).
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Comparative Analysis of Data Mining Classification Algorithms in Type-2 Diabetes Prediction Data Using WEKA Approach

Comparative Analysis of Data Mining Classification Algorithms in Type-2 Diabetes Prediction Data Using WEKA Approach

Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Of course, there will be problems. Many patterns will be banal and uninteresting. Others will be spurious, contingent on accidental coincidences in the particular dataset used. Data Mining is used to extract information from the raw data in databases—information that is expressed in a comprehensible form and can be used for a variety of purposes like as Type-2 Diabetes patients classified.
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An Expert System for Type 2 Diabetes Mellitus Risk Stratification: Data Mining Techniques

An Expert System for Type 2 Diabetes Mellitus Risk Stratification: Data Mining Techniques

Recently, Health care system is generating continuously massive amount of data (diabetes diseases related, health records etc.) for observing the different kind of diseases such as- diabetes risk prediction etc. and provide the scope for analysis of that data to extract the knowledge so that diseases can be observed by the different mining techniques and learning algorithms. . In order to decrease the morbidity and reduce the influence of diabetes mellitus, it is crucial for us to focus on a high-risk group of people with DM. So for that data mining techniques are gaining increasing importance in medical diagnosis field by their classification capability. In order to detect the high-risk group of DM, mining play vital role in order to utilize the information technology methods (classification methods machine learning methods etc.). Therefore, data mining technology is an appropriate study field for us. Data mining, also known as Knowledge Discovery in Databases (KDD), is defined as the computational process of discovering patterns in large datasets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The main purposes of these methods are pattern recognition, prediction, association, and clustering. Data mining contains a series of steps disposed automatically or semi-automatically in order to extract and discover interesting, unknown, hidden features from large quantities of data. It is reasonable to believe that there are various valuable patterns and waiting for researchers to explore them. Therefore it is necessary to establish a model that can classify the patient into two category such as- Diabetic person category and non-diabetic category. This study provides a way to solve to improve the accuracy of the prediction model, and to make the comparison of our model with others researches experiment.
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Comparing Three Data Mining Algorithms for Identifying 
the Associated Risk Factors of Type 2 Diabetes

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes

SVM is a data mining tool and is a supervised classification technique. This method can be used for prediction when the outcome variable is binary. SVM constructs multi-dimensional hyper-planes separating the two classes while maximizing the margin between the two classes. SVM uses kernel functions and has the ability to discriminate between classes that are not linearly separable [30] . The dataset is divided into two sets with the training dataset comprising of 6654 cases (70%) and testing dataset containing 2874 cases (30%). Each model is developed using the training dataset and tested using the testing dataset [31] . In case of each model, the incidence of type 2 diabetes is predicted, and confusion matrix is constructed in order to measure the accuracy of the model. Accuracy is measured as the proportion of cases classified correctly. Sensitivity is measured by the proportion of positive cases classified correctly, while specificity is determined by the proportion of negative cases classified correctly [7] . Mathematically, if TP stands for true positive, TN for true negative, FP for false positive, and FN for false negative, then accuracy = (TP + TN)/(TP + FP + TN + FN), sensitivity = TP/(TP + FN), and specificity = TN/(FP + TN).
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Diagnosis of Diabetes by using Data Mining Techniques

Diagnosis of Diabetes by using Data Mining Techniques

The Artificial neural network [1] is used as a human brain. It is one of the simplest definition and building blocks are neurons. There are 100 billions of neurons are available in the human mind. Every neuron has an association with rest of different neurons that are available in the human cerebrum. There are three neuron layers present in the neural system that are the input, hidden and output layer. Sonu kumari et al. [2] they are focusing on a data mining approach for diagnosis of diabetes.in their study neural network with back propagation classifier is used. In their dataset around 100 tuples are present. A Neural network consists of 28 nodes in which 13 input node, 13 hidden nodes and 1 output node. The result shows that the accuracy is 92.8%. Tanja Dujic et al. [3] in their proposed work artificial neural network was used to study the classification. Their main focus was on Type2 diabetes and pre-diabetes. How can control or detect these disease. For that, they are using feed forward artificial neural network which contains 2 layered architecture. And in the artificial neural network, there will be a hidden layer also. So they additionally deal with the
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Diagnosis of heart diseases using data mining

Diagnosis of heart diseases using data mining

The performance of various algorithms is listed below. Table.1. Performance Study of Data mining Algorithms The algorithm used Accuracy Time taken Naïve Bayes 52.33% 609ms Decision list 52% 719ms K-NN 45.67% 1000ms Diagnosis of heart disease was used Naïve Bayes, K-NN, Decision List in this Naïve Bayes has taken a time to run the data for accurate result when compared to other algorithms. Sudha et al. [11] to propose the classification algorithm like Naïve Bayes, Decision tree and Neural Network for predicting the stroke diseases. The classification algorithm like decision trees, Bayesian classifier and back propagation neural network were adopted in this study. The records with irrelevant data were removed from data warehouse before mining process occurs. Data mining classification technology consists of classification model and evaluation model. The classification model makes use of training data set in order to build classification predictive model. The testing data set was used for testing the classification efficiency. Then the classification algorithm like decision tree, naive Bayes and neural network was used for stroke disease prediction. The performance evaluation was carried out based on three algorithms and compared with various models used and accuracy was measured. While comparing these classification algorithms, the observation shows the neural network performance was more than the other two algorithms.
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GlucoGuide: An Intelligent Type-2 Diabetes Solution Using Data Mining and Mobile Computing

GlucoGuide: An Intelligent Type-2 Diabetes Solution Using Data Mining and Mobile Computing

Abstract—Type-2 Diabetes (T2D) is a dreadful disease affecting hundreds of millions of people worldwide, and is linked and worsen by unhealthy lifestyles. However, managing T2D effec- tively with lifestyle change remains highly challenging for both T2D patients and doctors. In this paper, we proposed, built, and evaluated a personalized diabetes recommendation system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates a variety of lifestyle data via medical sensors and mobile devices, mines the data with a novel data-mining frame- work, and outputs personalized and timely recommendations to patients aimed to control their blood glucose level. To evaluate its clinical efficiency, we conducted a three-month clinical trial on human subjects. Due to the high cost and complexity of trials on human, a small but representative subject group was involved. Two standard laboratory blood tests for diabetes were used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months.
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Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

Data mining and machine learning algorithms in the medical field extracts different hidden patterns from the medical data. They can be used for the analysis of important clinical parameters, prediction of various diseases, forecasting tasks in medicine, extraction of medical knowledge, therapy planning support and patient management. A number of algorithms were proposed for the prediction and diagnosis of diabetes. These algorithms provide more accuracy than the available traditional systems. This paper includes algorithms like Expectation Maximization Algorithm, K Nearest Neighbor algorithm, K-means algorithm, Amalgam KNN algorithm and Adaptive Neuro Fuzzy Inference System algorithm. From the observation EM possess the least classification accuracy and amalgam KNN and ANFIS provide the better classification accuracy results. Amalgam KNN comprises both the feature of KNN and K means. ANFIS in cooperates both the features
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Validity of type 2 diabetes diagnosis in a population-based electronic health record database

Validity of type 2 diabetes diagnosis in a population-based electronic health record database

The ability of EHR to accurately identify incident cases of diagnosed type 2 diabetes was investigated in a cohort from Navarra that had been included in a large prospect- ive type 2 diabetes case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC-InterAct study) [19]. This is a large multi-center study to investigate how genetic and lifestyle behavioral factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes. The Navarra EPIC cohort included 8084 partici- pants (3908 men and 4176 women) aged 45–65 years at the time of enrollment (1992–1995). Most of the partici- pants were blood donors (75%), and the rest were civil ser- vants and general population. More detailed information about the EPIC study methods have been described else- where [20, 21]. A sensitive approach was used with the aim of identifying all potential incident diabetes cases be- tween the recruitment and December 2007 using multiple sources: self-reported diabetes or use of diabetes medica- tion in a follow-up survey carried out 3 years after recruit- ment, diabetes diagnosis in the hospital discharge databases, type 2 diabetes (T90), type 1 diabetes (T89) and T99 (descriptive term “glucose intolerance”) diagnosis in primary care EHRs, prescription of antidiabetic drugs and cause-of-death register. A team of trained health profes- sionals reviewed the clinical data to verify if the cases ful- filled the criteria proposed by the American Diabetes Association (ADA) in 2003: 1) Symptoms of diabetes (e.g.:
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Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

Radha and Rajagopalan [4] introduced an application of fuzzy logic to diagnosis of diabetes. It describes the fuzzy sets and linguistic variables that contribute to the diagnosis of disease particularly diabetes. As we all know fuzzy logic is a computational paradigm that pro- vides a tool based on mathematics which deals with un- certainty. At the same time this paper also presents a computer-based Fuzzy Logic with maximum and mini- mum relationship, membership values consisting of the components, specifying fuzzy set frame work. Forty pa- tients’ data have been collected to make this relationship more strong.
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A data mining approach for cardiovascular diagnosis

A data mining approach for cardiovascular diagnosis

Abstract: The large amounts of data generated by health- care transactions are too complex and voluminous to be processed and analysed by traditional methods. Data min- ing can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the pre- diction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hos- pital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To de- velop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Work- bench since this one allows users to quickly try out and compare different machine learning methods on new data sets
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Metabolic syndrome, dyslipidemia, hypertension and type 2 diabetes in youth: from diagnosis to treatment

Metabolic syndrome, dyslipidemia, hypertension and type 2 diabetes in youth: from diagnosis to treatment

Physicians must pay attention, as adolescents change their mind all the time, and they must practice the type of physical activity they decide to, and switch it when- ever they want. Very often, patients do not want or do not like doing exercise. It is necessary to identify the restraints which keep adolescents from participating in activities [46-48]. It is important to emphasize to patients and their family that exercise does not have to be competitive, and that the main goal is to improve physical conditioning, to reduce weight and comorbid- ities. The compliance to physical exercise, at this age range is the same as in adults, i.e., 20 to 80%. Fre- quently, obese children or adolescents have low physical conditioning. Their perception of effort is higher than the perception of thin individuals, and skeletal muscle pains are common, as a result of excess weight [49]. Therefore, it is important to establish progressive goals (mainly regarding intensity and volume of activities) until each patient meets the goals assigned to them.
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Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management

Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management

The spectrum of UTI in these patients ranges from asymptomatic bacteriuria (ASB) to lower UTI (cystitis), pyelonephritis, and severe urosepsis. Serious complications of UTI, such as emphysematous cystitis and pyelonephritis, renal abscesses and renal papillary necrosis, are all encountered more frequently in type 2 diabetes than in the general population. 12,13 Type 2 diabetes is not only a risk factor for community-acquired

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<p>Detection of Secondary Metabolites as Biomarkers for the Early Diagnosis and Prevention of Type 2 Diabetes</p>

<p>Detection of Secondary Metabolites as Biomarkers for the Early Diagnosis and Prevention of Type 2 Diabetes</p>

Many plasma metabolites were reported as biomarkers associated with the development of T2D. 9–16 Alterations in these metabolites can be traced via their biological path- ways, examples of which are the amino acid catabolism 9,11 – 14,16,17 and hexose metabolism. 12,15 Previous studies indicated that plasma glycine level is a negative biomarker in patients with diabetes mellitus. 18–21 Glycine metabolism pathway indicates the participation of many other important metabolites that mostly serve in the biosynthesis of glycine. However, many of these metabolites have not been proven to be associated with insulin resistance or diabetes. 22 In T2D patients, low plasma glycine level was found to be nega- tively correlated with insulin sensitivity. 23,24 Insulin sensi- tizer therapy with pioglitazone + metformin for patients with T2D resulted in increased plasma glycine concentra- tion as compared to placebo, 25 while reduced plasma gly- cine level was found to be a predictor for impaired glucose tolerance and T2D incidence. 12,26,27 The agency of the European Prospective Investigation into Cancer and Nutrition claimed that higher serum glycine is associated with decreased risk of incident T2D as glycine receptors existing in human pancreatic β -cells might stimulate or provoke secretion of insulin. 28 In addition, the reduced plasma betaine level was also found to act as a positive biomarker of diabetes mellitus and proved to be associated with incident T2D. 16,21
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Plasma miR-135a; a potential biomarker for diagnosis of new type 2 diabetes (T2DM)

Plasma miR-135a; a potential biomarker for diagnosis of new type 2 diabetes (T2DM)

A total of 120 subjects were included in this case-control study. The subjects were divided into 4 groups, which were healthy individuals, people with a newly diagnosed type 2 diabetes in the last 6 months and not taking any hypoglycemic drugs, people with impaired fasting glucose (IFG), and people with impaired glucose tolerance (IGT). The study took place in the Center of Metabolic, Qazvin, Iran. The ethical code was REC.1394.191 granted from Qazvin Medical University. Informed consent was taken from all the subjects.

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Type 2 Diabetes in Children and Adolescents: Risk Factors, Diagnosis, and Treatment

Type 2 Diabetes in Children and Adolescents: Risk Factors, Diagnosis, and Treatment

Regardless of the initial treatment chosen for E.J., a critical factor in her management of diabetes is likely to be the support and involvement of her fam- ily. From the case description, it seems that E.J. lives in an intact, two-parent home, which implies that some parental support for managing her diabetes is likely. Although having other people in the home who have been diagnosed with diabetes might be an advantage in learning the components of diabetes management (e.g., diet, blood glucose testing, injection techniques), the fact that E.J.’s mother has developed chronic diabetic complications might indicate that her own glycemic management has been less than optimal for some time. Multiple risk factors clearly indicate that testing of E.J.’s father and brother treatment with insulin in achieving a
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The Early Augmentationfor Diabetes Diagnosis
Using Data Mining Approaches

The Early Augmentationfor Diabetes Diagnosis Using Data Mining Approaches

furthermore stresses how 1-standard SVM can be utilized as a part of highlight choice and smooth SVM (SSVM) for characterization. Two issues tended to here are, the first is to distinguish the significance of the parameters on the bosom tumor. The second research issue is to analyze bosom malignancy in light of nine characteristics of Wisconsin bosom disease dataset. To recognize the significance of the parameters, the 1-standard SVM of the first data was finished. The more grounded parameters are as per the following: parameter 1 (Clump thickness), parameter 3 (Uniformity of Cell shape), parameter 6 (Bare Nuclei), parameter 7 (Bland Chromatin), and parameter 9 (Mitoses), while parameter 2 (Uniformity of Belsize), parameter 4 (Marginal Adhesion), parameter 5 (Single Epithelial Cell Size) and parameter 8 (Normal Nucleoli) are weaker. The got preparing and testing order exactness utilizing 10 fold cross approval were 97.52% and 97.01% individually. When one of the feeble parameters was expelled both preparing and testing demonstrates a little diminishing in precision [5].
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Tests for Screening and Diagnosis of Type 2 Diabetes

Tests for Screening and Diagnosis of Type 2 Diabetes

In addition to identification of the appropriate diagnostic test, another practical consideration is deter- mination of the diabetes-defining threshold. Some studies have evalu- ated cut-points that are two standard deviations above normal, and others have used points that represent a natural break between normal and hyperglycemic peaks in populations with a high incidence of diabetes. However, the theoretical clinical ideal would be to estimate the point above which treatment specific to diabetic patients would signifi- I n B r I e f

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