Identification of metabolic syndrome using decision tree analysis

Worachartcheewan, Apilak; Nantasenamat, Chanin; Isarankura‐Na‐Ayudhya, Chartchalerm; Pidetcha, Phannee; Prachayasittikul, Virapong

doi:10.1016/j.diabres.2010.06.009

Cited by 46 publications

(46 citation statements)

References 16 publications

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“…The above defined 24 characteristics were used as independent variables with intentional insulin omission used as the dependent variable. A decision tree is a supervised approach that uses a set of if‐then rules to classify samples into categories of interest . Determination of sensitivity and specificity were used to evaluate the predictive performance of the decision tree.…”

Section: Methodsmentioning

confidence: 99%

Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus

Pinhas‐Hamiel

Hamiel

Greenfield

et al. 2013

Intl J Eating Disorders

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Section: Methodsmentioning

confidence: 99%

Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus

Pinhas‐Hamiel

Hamiel

Greenfield

et al. 2013

Intl J Eating Disorders

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“…12 The methodological basis of DM is the discernment of patterns and relationships in large quantities of data that results in the construction of models that can help the assignment of class labels through the use of statistical methods, artificial intelligence to the management of databases. 14 Both text mining and DM involve the use of a computer to access and organize data. Text mining is essentially the discovery of new or unknown information from large amounts of different unstructured textual resources.…”

Section: Generating New Knowledge For Decision Makingmentioning

confidence: 99%

The Influence of Technology in Nursing Education

Krau

2015

Nursing Clinics of North America

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“…The basis of the methodologies of data mining is its ability to find patterns and relationships within large quantities of data that can enable the construction of models that meet the task of assigning the class label at unlabeled cases, the combination of statistical methods and artificial intelligence to the management of databases [18,19]. …”

Section: Introductionmentioning

confidence: 99%

Applying data mining techniques to improve diagnosis in neonatal jaundice

Ferreira

Oliveira

Freitas

2012

BMC Med Inform Decis Mak

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BackgroundHyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.MethodsThis study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia.ResultsThe application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic.ConclusionsThe findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

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Identification of metabolic syndrome using decision tree analysis

Cited by 46 publications

References 16 publications

Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus

Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus

The Influence of Technology in Nursing Education

Applying data mining techniques to improve diagnosis in neonatal jaundice

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