Applying Data Mining Techniques to Improve Diagnosis in Neonatal Jaundice

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2012 Dec 11

PMID 23216895

Citations 19

Authors

Duarte Ferreira

Abilio Oliveira

Alberto Freitas

Affiliations

Soon will be listed here.

Abstract

Background: 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 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.

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.

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References

Oztekin A, Delen D, Kong Z . Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology. Int J Med Inform. 2009; 78(12):e84-96. DOI: 10.1016/j.ijmedinf.2009.04.007. View

Jeffrey Maisels M . Screening and early postnatal management strategies to prevent hazardous hyperbilirubinemia in newborns of 35 or more weeks of gestation. Semin Fetal Neonatal Med. 2009; 15(3):129-35. DOI: 10.1016/j.siny.2009.10.004. View

Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Pidetcha P, Prachayasittikul V . Identification of metabolic syndrome using decision tree analysis. Diabetes Res Clin Pract. 2010; 90(1):e15-8. DOI: 10.1016/j.diabres.2010.06.009. View

Rennie J, Burman-Roy S, Stephen Murphy M . Neonatal jaundice: summary of NICE guidance. BMJ. 2010; 340:c2409. DOI: 10.1136/bmj.c2409. View

. Management of hyperbilirubinemia in the newborn infant 35 or more weeks of gestation. Pediatrics. 2004; 114(1):297-316. DOI: 10.1542/peds.114.1.297. View

Bhutani V, Johnson L, Sivieri E . Predictive ability of a predischarge hour-specific serum bilirubin for subsequent significant hyperbilirubinemia in healthy term and near-term newborns. Pediatrics. 1999; 103(1):6-14. DOI: 10.1542/peds.103.1.6. View

Randev S, Grover N . Predicting neonatal hyperbilirubinemia using first day serum bilirubin levels. Indian J Pediatr. 2010; 77(2):147-50. DOI: 10.1007/s12098-009-0335-3. View

Delen D, Oztekin A, Kong Z . A machine learning-based approach to prognostic analysis of thoracic transplantations. Artif Intell Med. 2010; 49(1):33-42. DOI: 10.1016/j.artmed.2010.01.002. View

Keren R, Luan X, Friedman S, Saddlemire S, Cnaan A, Bhutani V . A comparison of alternative risk-assessment strategies for predicting significant neonatal hyperbilirubinemia in term and near-term infants. Pediatrics. 2008; 121(1):e170-9. DOI: 10.1542/peds.2006-3499. View

10.

Kuzniewicz M, Escobar G, Wi S, Liljestrand P, McCulloch C, Newman T . Risk factors for severe hyperbilirubinemia among infants with borderline bilirubin levels: a nested case-control study. J Pediatr. 2008; 153(2):234-40. PMC: 3142930. DOI: 10.1016/j.jpeds.2008.01.028. View

11.

Delen D, Walker G, Kadam A . Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med. 2005; 34(2):113-27. DOI: 10.1016/j.artmed.2004.07.002. View

12.

Newman T, Liljestrand P, Escobar G . Combining clinical risk factors with serum bilirubin levels to predict hyperbilirubinemia in newborns. Arch Pediatr Adolesc Med. 2005; 159(2):113-9. DOI: 10.1001/archpedi.159.2.113. View

13.

Burke B, Robbins J, Bird T, Hobbs C, Nesmith C, Tilford J . Trends in hospitalizations for neonatal jaundice and kernicterus in the United States, 1988-2005. Pediatrics. 2009; 123(2):524-32. DOI: 10.1542/peds.2007-2915. View

14.

McNeil B, Hanley J, Funkenstein H, Wallman J . Paired receiver operating characteristic curves and the effect of history on radiographic interpretation. CT of the head as a case study. Radiology. 1983; 149(1):75-7. DOI: 10.1148/radiology.149.1.6611955. View

15.

Besser I, Perry Z, Mesner O, Zmora E, Toker A . Yield of recommended blood tests for neonates requiring phototherapy for hyperbilirubinemia. Isr Med Assoc J. 2010; 12(4):220-4. View

16.

Chou S, Palmer R, Ezhuthachan S, Newman C, Pradell-Boyd B, Jeffrey Maisels M . Management of hyperbilirubinemia in newborns: measuring performance by using a benchmarking model. Pediatrics. 2003; 112(6 Pt 1):1264-73. DOI: 10.1542/peds.112.6.1264. View

17.

. Detection and treatment of neonatal jaundice. Lancet. 2010; 375(9729):1845. DOI: 10.1016/S0140-6736(10)60852-5. View

18.

Manning D, Todd P, Maxwell M, Platt M . Prospective surveillance study of severe hyperbilirubinaemia in the newborn in the UK and Ireland. Arch Dis Child Fetal Neonatal Ed. 2006; 92(5):F342-6. PMC: 2675352. DOI: 10.1136/adc.2006.105361. View

19.

Vianna R, Moro C, Moyses S, Carvalho D, Nievola J . [Data mining and characteristics of infant mortality]. Cad Saude Publica. 2010; 26(3):535-42. DOI: 10.1590/s0102-311x2010000300011. View

20.

Bhutani V, Vilms R, Hamerman-Johnson L . Universal bilirubin screening for severe neonatal hyperbilirubinemia. J Perinatol. 2010; 30 Suppl:S6-15. DOI: 10.1038/jp.2010.98. View