title: Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study.
abstract: BACKGROUND
  Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients.
  
  
  METHODS
  Health records from the Korea National Health and Nutrition Examination Surveys (KNHANES) V-1 were used. The prediction models for DR were constructed using data from 327 diabetic patients, and were validated internally on 163 patients in the KNHANES V-1. External validation was performed using 562 diabetic patients in the KNHANES V-2. The learning models, including ridge, elastic net, and LASSO, were compared to the traditional indicators of DR.
  
  
  RESULTS
  Considering the Bayesian information criterion, LASSO predicted DR most efficiently. In the internal and external validation, LASSO was significantly superior to the traditional indicators by calculating the area under the curve (AUC) of the receiver operating characteristic. LASSO showed an AUC of 0.81 and an accuracy of 73.6% in the internal validation, and an AUC of 0.82 and an accuracy of 75.2% in the external validation.
  
  
  CONCLUSIONS
  The sparse learning model using LASSO was effective in analyzing the epidemiological underlying patterns of DR. This is the first study to develop a machine learning model to predict DR risk using health records. LASSO can be an excellent choice when both discriminative power and variable selection are important in the analysis of high-dimensional electronic health records.
authors.affiliation: Department of Medicine, Yonsei University College of Medicine, Seoul, South Korea. fawoo2@yuhs.ac.
authors.forename: Ein
authors.initials: E
authors.lastname: Oh
authors.forename: Tae Keun
authors.initials: TK
authors.lastname: Yoo
authors.forename: Eun-Cheol
authors.initials: EC
authors.lastname: Park
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003430:Cross-Sectional Studies; D003930:Diabetic Retinopathy / Q000175:diagnosis / Q000453:epidemiology; D005654:Fundus Oculi; D006801:Humans; D008499:Medical Records / Q000706:statistics & numerical data; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D056910:Republic of Korea; D012306:Risk
references.citation: IEEE Trans Neural Netw. 2004 Jan;15(1):16-28
references.pmid: 15387244
references.citation: JAMA. 1999 Jun 2;281(21):2005-12
references.pmid: 10359389
references.citation: Contemp Clin Trials. 2012 Sep;33(5):969-75
references.pmid: 22575797
references.citation: Diabetes Care. 1984 Nov-Dec;7(6):533-8
references.pmid: 6510179
references.citation: Diabetes Care. 2008 Feb;31(2):193-8
references.pmid: 18024852
references.citation: Bioinformatics. 2011 Dec 15;27(24):3399-406
references.pmid: 22156367
references.citation: Curr Diabetes Rev. 2010 Jan;6(1):42-51
references.pmid: 20034368
references.citation: Am J Hypertens. 2002 Mar;15(3):244-50
references.pmid: 11939615
references.citation: Am J Ophthalmol. 2001 Nov;132(5):760-76
references.pmid: 11704039
references.citation: Lancet. 2010 Jul 10;376(9735):124-36
references.pmid: 20580421
references.citation: JAMA. 2007 Aug 22;298(8):902-16
references.pmid: 17712074
references.citation: Diabetologia. 2011 Oct;54(10):2525-32
references.pmid: 21792613
references.citation: Ophthalmology. 1991 May;98(5 Suppl):786-806
references.pmid: 2062513
references.citation: Diabetologia. 1993 Oct;36(10):883-92
references.pmid: 8243865
references.citation: Acta Diabetol. 2013 Dec;50(6):837-42
references.pmid: 23354926
references.citation: Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S498-504
references.pmid: 10993420
references.citation: Diabetes Care. 2012 Jan;35 Suppl 1:S64-71
references.pmid: 22187472
references.citation: Bioinformatics. 2012 Jan 15;28(2):229-37
references.pmid: 22155867
references.citation: Invest Ophthalmol Vis Sci. 2004 Mar;45(3):910-8
references.pmid: 14985310
references.citation: Psychol Methods. 2012 Jun;17(2):228-43
references.pmid: 22309957
references.citation: Diabetes Care. 2005 Jul;28(7):1739-44
references.pmid: 15983328
references.citation: JAMA. 2010 Aug 11;304(6):649-56
references.pmid: 20699456
references.citation: Korean J Ophthalmol. 2011 Dec;25(6):421-33
references.pmid: 22131780
references.citation: J Mol Biol. 2012 Sep 7;422(1):145-55
references.pmid: 22609437
references.citation: J Res Med Sci. 2009 Mar;14(2):105-10
references.pmid: 21772868
references.citation: Genet Epidemiol. 2010 Dec;34(8):879-91
references.pmid: 21104890
references.citation: Diabetes Res Clin Pract. 1996 May;32(3):157-63
references.pmid: 8858204
references.citation: PLoS One. 2012;7(5):e36718
references.pmid: 22590593
references.citation: Curr Opin Ophthalmol. 2006 Dec;17(6):519-22
references.pmid: 17065919
references.citation: IEEE/ACM Trans Comput Biol Bioinform. 2010 Oct-Dec;7(4):636-46
references.pmid: 21030731
references.citation: IEEE Trans Med Imaging. 2011 May;30(5):1154-65
references.pmid: 21478072
references.citation: PLoS One. 2012;7(8):e41694
references.pmid: 22916111
references.citation: BMC Bioinformatics. 2013 Feb 15;14:55
references.pmid: 23419172
references.citation: Invest Ophthalmol Vis Sci. 1998 Feb;39(2):233-52
references.pmid: 9477980
references.citation: Diabetes. 1989 Oct;38(10):1231-7
references.pmid: 2792575
references.citation: Surgery. 2011 Jan;149(1):87-93
references.pmid: 20466403
references.citation: Biom J. 2005 Aug;47(4):458-72
references.pmid: 16161804
references.citation: Diabetes Care. 2001 Nov;24(11):1917-22
references.pmid: 11679457
references.citation: Diabetes Care. 2004 Jan;27 Suppl 1:S84-7
references.pmid: 14693935
references.citation: Diabetes Care. 2011 Jan;34(1):145-50
references.pmid: 20978099