BMC Med Inform Decis Mak - Applying data mining techniques to improve diagnosis in neonatal jaundice.

Tópicos

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Resumo

CKGROUND: Hyperbilirubinemia 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.METHODS: This 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 T?mega 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.RESULTS: The 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.CONCLUSIONS: The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

Resumo Limpo

ckground hyperbilirubinemia emerg increas common problem newborn due decreas hospit length stay birth jaundic common diseas newborn although benign case can lead sever neurolog consequ poor evalu differ area medicin data mine contribut improv result obtain methodologiesh aim studi improv diagnosi neonat jaundic applic data mine techniquesmethod studi follow differ phase cross industri standard process data mine model methodologythi observ studi perform obstetr depart central hospit centro hospitalar tmega e sousaep februari march total healthi newborn infant week gestat enrol studi variabl collect analyz also transcutan bilirubin level measur birth hospit discharg maximum time interv hour measur use noninvas bilirubinometerdiffer attribut subset use train test classif model use algorithm includ weka data mine softwar decis tree j neural network multilay perceptron accuraci result compar tradit method predict hyperbilirubinemiaresult applic differ classif algorithm collect data allow predict subsequ hyperbilirubinemia high accuraci particular hour life newborn accuraci predict hyperbilirubinemia best result obtain use follow algorithm naiv bay multilay perceptron simpl logisticconclus find studi sustain new approach data mine may support medic decis contribut improv diagnosi neonat jaundic

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