791
Views
22
CrossRef citations to date
0
Altmetric
Reviews

Introduction to Machine Learning for Ophthalmologists

ORCID Icon, &
Pages 19-41 | Received 11 Dec 2017, Accepted 15 Nov 2018, Published online: 30 Nov 2018

References

  • Gr Graham B Kaggle diabetic retinopathy detection competition report. University of Warwick. https://kaggle2.blob.core.windows.net/forum-message-attachments/88655/2795/competitionreport.pdf Accessed 2015.
  • Zheng C, Rashid N, Wu Y, et al. Using natural language processing and machine learning to identify gout flares from electronic clinical notes. Arthritis Care Res (Hoboken). 2014;66(11):1740–1748. doi:10.1002/acr.22324.
  • Danielewska ME, Iskander DR, Krzyzanowska-Berkowska P. Age-related changes in corneal pulsation: ocular dicrotism. Optom Vis Sci. 2014;91(1):54–59. doi:10.1097/OPX.0000000000000113.
  • Futoma J, Sendak M, Cameron CB, Heller K Predicting disease progression with a model for multivariate longitudinal clinical data. Proceedings of the 1st Machine Learning for Healthcare Conference, Los Angeles, CA, USA; 2016.
  • Dhar V. Data science and prediction. Commun ACM. 2013;56(12):64–73. doi:10.1145/2500499.
  • Russel S, Norvig P. Artificial Intelligence: A Modern Approach. EUA: Prentice Hall, New Jersey, NJ, USA; 2013
  • Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3(3):210–229. doi:10.1147/rd.33.0210.
  • Kohavi R, Provost F. Glossary of terms. Mach Learn. 1998;30(2–3):271–274. doi:10.1023/A:1017181826899.
  • Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999;31(3):264–323. doi:10.1145/331499.331504.
  • Zhang S, Zhang C, Yang Q. Data preparation for data mining. Appl Artif Intell. 2003;17(5–6):375–381. doi:10.1080/713827180.
  • Hodge V, Austin J. A survey of outlier detection methodologies. Artif Intell Rev. 2004;22(2):85–126. doi:10.1023/B:AIRE.0000045502.10941.a9.
  • Schiff GD, Volk LA, Volodarskaya M, et al. Screening for medication errors using an outlier detection system. J Am Med Inf Assoc. 2017;24(2):281–287.doi:10.1093/jamia/ocw171.
  • Chen T, Guestrin C Xgboost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; ACM, California, USA; 2016.
  • Little RJ, Rubin DB. Statistical Analysis with Missing Data. John Wiley & Sons, USA; 2014
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 1977:1–38. jstor.org/stable/2984875.
  • Donders ART, van der Heijden GJMG, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087–1091. doi:10.1016/j.jclinepi.2006.01.014.
  • Batista GE, Monard MC. An analysis of four missing data treatment methods for supervised learning. Appl Artif Intell. 2003;17(5–6):519–533. doi:10.1080/713827181.
  • Yu L, Liu H. Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res. 2004;5:1205–1224. http://www.jmlr.org/papers/volume5/yu04a/yu04a.pdf.
  • Harman HH. Modern Factor Analysis. University of Chicago press, Chicago, USA; 1960
  • Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B. 1996:267–288. jstor.org/stable/2346178.
  • Jain A, Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell. 1997;19(2):153–158. doi:10.1109/34.574797.
  • Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–1182. http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf.
  • Koller D, Sahami M Toward optimal feature selection. Technical Report. Stanford InfoLab, USA; 1996.
  • Jolliffe I. Principal Component Analysis. Wiley Online Library, USA; 2002
  • Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507–2517. doi:10.1093/bioinformatics/btm344.
  • Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106. doi:10.1007/BF00116251.
  • Rozema JJ, Zakaria N, Ruiz Hidalgo I, Jongenelen S, Tassignon MJ, Koppen C. How abnormal is the noncorneal biometry of keratoconic eyes? Cornea. 2016;35(6):860–865. doi:10.1097/ICO.0000000000000802.
  • Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi:10.1023/A:1010933404324.
  • Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–1232. https://www.jstor.org/stable/pdf/2699986.pdf?casa_token=CAenytmOR3wAAAAA:DLOKBG6D_lzsYx-XCvSDgfpkam3KNhbnscFM1EckLTYQ4jCwieE8RJ7frGaKMm-vXuA0Xd3zuFO_SXfPda0Lgv6slqKsdje3gRPoyQfJr1gae8IXZA.
  • Haykin S. Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, NJ, USA; 2004
  • Olazaran M. A sociological study of the official history of the perceptrons controversy. Soc Stud Sci. 1996;26(3):611–659. doi:10.1177/030631296026003005.
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nevada, USA; 2016.
  • Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82–97. doi:10.1109/MSP.2012.2205597.
  • Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. International Conference on Learning Representations (ICLR) 2015. San Diego, CA, USA arXiv:14090473. 2014.
  • Shazeer N, Mirhoseini A, Maziarz K, et al. Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. International Conference on Learning Representations (ICLR) 2017. Toulon, France arXiv:170106538. 2017.
  • Kohonen T. Self-organizing map. Proc IEEE. 1990;78:1464–1480. doi:10.1109/5.58325.
  • Langley P, Sage S Induction of selective Bayesian classifiers. Proceedings of the Tenth international conference on Uncertainty in artificial intelligence; Morgan Kaufmann Publishers Inc.; 1994.
  • Hand DJ, Yu K. Idiot’s Bayes—not so stupid after all? Int Stat Rev. 2001;69(3):385–398. doi:10.2307/1403452.
  • Airoldi EM. Getting started in probabilistic graphical models. PLoS Comput Biol. 2007;3(12):e252. doi:10.1371/journal.pcbi.0030252.
  • Burges CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2(2):121–167. doi:10.1023/A:1009715923555.
  • Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Informatica. 2007;31:249–268. http://www.informatica.si/index.php/informatica/article/viewFile/148/140.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi:10.1023/A:1022627411411.
  • Hofmann T, Schölkopf B, Smola AJ. Kernel methods in machine learning. Ann Stat. 2008;36:1171–1220. doi:10.1214/009053607000000677.
  • Abakar KA, Yu C. Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity. Ijftr. 2014;39:55–59. http://nopr.niscair.res.in/bitstream/123456789/27358/1/IJFTR%2039(1)%2055-59.pdf.
  • Teknomo K. K-means clustering tutorial. Medicine (Baltimore). 2006;100:3.
  • Ding C, He X K-means clustering via principal component analysis. Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada; 2004, 1–9.
  • Amancio DR, Comin CH, Casanova D, et al. A systematic comparison of supervised classifiers. PloS one. 2014;9(4):e94137. doi:10.1371/journal.pone.0094137.
  • Melcer T, Danielewska ME, Iskander DR. Wavelet representation of the corneal pulse for detecting ocular dicrotism. PloS one. 2015;10(4):e0124721. doi:10.1371/journal.pone.0124721.
  • Hanczar B, Hua J, Sima C, Weinstein J, Bittner M, Dougherty ER. Small-sample precision of ROC-related estimates. Bioinformatics. 2010;26(6):822–830. doi:10.1093/bioinformatics/btq037.
  • Lobo JM, Jiménez‐Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008;17(2):145–151. doi:10.1111/j.1466-8238.2007.00358.x.
  • Stone M. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B. 1974:111–147. https://www.jstor.org/stable/2984809.
  • Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7:1–26. doi:10.1214/aos/1176344552.
  • Lachenbruch PA, Mickey MR. Estimation of error rates in discriminant analysis. Technometrics. 1968;10(1):1–11. doi:10.2307/1266219.
  • Kohavi R A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada; 1995.
  • Goldbaum MH, Sample PA, White H, Weinreb R. Discrimination of normal and glaucomatous visual fields by neural network. Invest Ophthalmol Vis Sci. 1990;31:503.
  • Goldbaum MH, Sample PA, White H, et al. Interpretation of automated perimetry for glaucoma by neural network. Invest Ophthalmol Vis Sci. 1994;35:3362–3373.
  • Goldbaum MH, Sample PA, Chan K, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci. 2002;43:162–169.
  • Lietman T, Eng J, Katz J, Quigley HA. Neural networks for visual field analysis: how do they compare with other algorithms?. J Glaucoma. 1999;8:77–80.
  • Henson DB, Spenceley SE, Bull DR. Spatial classification of glaucomatous visual field loss. Br J Ophthalmol. Jun 1996;80(6):526–531.
  • Bowd C, Chan K, Zangwill LM, et al. Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. Invest Ophthalmol Vis Sci. 2002;43:3444–3454.
  • Huang M, Chen H. Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Invest Ophthalmol Vis Sci. 2005;46(11):4121–4129. doi:10.1167/iovs.05-0069.
  • Huang M, Chen H, Lin J. Rule extraction for glaucoma detection with summary data from StratusOCT. Invest Ophthalmol Vis Sci. 2007;48(1):244–250. doi:10.1167/iovs.06-0320.
  • Burgansky-Eliash Z, Wollstein G, Chu T, et al. Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. Invest Ophthalmol Vis Sci. 2005;46(11):4147–4152. doi:10.1167/iovs.05-0366.
  • Khalil T, Khalid S, Syed AM Review of machine learning techniques for glaucoma detection and prediction. Science and Information Conference (SAI), IEEE, London, United Kingdom; 2014.
  • Kumar BN, Chauhan R, Dahiya N. Detection of glaucoma using image processing techniques: a critique. Semin Ophthalmol. 2018;33(2):275–283. doi:10.1080/08820538.2016.1229801.
  • Goldbaum MH, Jang GJ, Bowd C, et al. Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis. Trans Am Ophthalmol Soc. 2009;107:136–144.
  • Elze T, Pasquale LR, Shen LQ, Chen TC, Wiggs JL, Bex PJ. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface. 2015;12:103. doi:10.1098/rsif.2014.1118.
  • Yousefi S, Balasubramanian M, Goldbaum MH, et al. Unsupervised Gaussian mixture-model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields. Transl Vis Sci Technol. 2016;5(3):2. doi:10.1167/tvst.5.3.2.
  • Asaoka R, Hirasawa K, Iwase A, et al. Validating the usefulness of the “Random Forests” classifier to diagnose early glaucoma with optical coherence tomography. Am J Ophthalmol. 2017;174:95–103. doi:10.1016/j.ajo.2016.11.001.
  • Bowd C, Zangwill LM, Medeiros FA, et al. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. Invest Ophthalmol Vis Sci. 2004;45(7):2255–2262. doi:10.1167/iovs.03-1087.
  • Williamson TH, Keating D. Telemedicine and computers in diabetic retinopathy screening. Br J Ophthalmol. 1998;82:5–6.
  • Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol. 1996;80:940–944.
  • Jelinek HF, Rocha A, Carvalho T, Goldenstein S, Wainer J Machine learning and pattern classification in identification of indigenous retinal pathology. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, Boston, MA, USA; 2011.
  • Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MS, Abramoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Invest Ophthalmol Vis Sci. 2007;48(5):2260–2267. doi:10.1167/iovs.06-0996.
  • Sopharak A, Dailey MN, Uyyanonvara B, et al. Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J Mod Opt. 2010;57(2):124–135. doi:10.1080/09500340903118517.
  • Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004;21(1):84–90. doi:10.1046/j.1464-5491.2003.01085.x.
  • Yun WL, Acharya UR, Venkatesh YV, Chee C, Min LC, Ng EYK. Identification of different stages of diabetic retinopathy using retinal optical images. Inf Sci. 2008;178(1):106–121. doi:10.1016/j.ins.2007.07.020.
  • Roychowdhury S, Koozekanani DD, Parhi KK. Dream: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform. 2014;18(5):1717–1728. doi:10.1109/JBHI.2013.2294635.
  • Acharya UR, Lim CM, Ng EYK, Chee C, Tamura T. Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc Inst Mech Eng H. 2009;223(5):545–553. doi:10.1243/09544119JEIM486.
  • Quellec G, Lamard M, Abràmoff MD, et al. A multiple-instance learning framework for diabetic retinopathy screening. Med Image Anal. 2012;16(6):1228–1240. doi:10.1016/j.media.2012.06.003.
  • Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216.
  • Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–5206. doi:10.1167/iovs.16-19964.
  • Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124(7):962–969. doi:10.1016/j.ophtha.2017.02.008.
  • Jani PD, Forbes L, Choudhury A, Preisser JS, Viera AJ, Garg S. Evaluation of diabetic retinal screening and factors for ophthalmology referral in a telemedicine network. JAMA Ophthalmol. 2017;135(7):706–714. doi:10.1001/jamaophthalmol.2017.1150.
  • Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng E, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med. 2013;43(12):2136–2155. doi:10.1016/j.compbiomed.2013.10.007.
  • Lahmiri S, Boukadoum M. Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions. Biomed Eng Tech. 2014;59(4):357–366. doi:10.1515/bmt-2013-0082.
  • Mookiah MRK, Acharya UR, Koh JEW, et al. Decision support system for age-related macular degeneration using discrete wavelet transform. Med Biol Eng Comput. 2014;52(9):781–796. doi:10.1007/s11517-014-1180-8.
  • Feeny AK, Tadarati M, Freund DE, Bressler NM, Burlina P. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput Biol Med. 2015;65:124–136. doi:10.1016/j.compbiomed.2015.06.018.
  • Fraccaro P, Nicolo M, Bonetto M, et al. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach. BMC Ophthalmol. 2015;15(1):10. doi:10.1186/1471-2415-15-10.
  • Maeda N, Klyce SD, Smolek MK. Neural network classification of corneal topography. Preliminary demonstration. Invest Ophthalmol Vis Sci. 1995;36:1327–1335.
  • Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci. 1997;38:2290–2299.
  • Twa MD, Parthasarathy S, Roberts C, Mahmoud AM, Raasch TW, Bullimore MA. Automated decision tree classification of corneal shape. Optom Vis Sci. 2005;82:1038–1046.
  • Marsolo K, Twa M, Bullimore MA, Parthasarathy S. Spatial modeling and classification of corneal shape. IEEE Trans Inf Technol Biomed. 2007;11(2):203–212. doi:10.1109/TITB.2006.879591.
  • Saad A, Guilbert E, Gatinel D. Corneal enantiomorphism in normal and keratoconic eyes. J Refract Surg. 2014;30(8):542–547. doi:10.3928/1081597X-20140711-07.
  • Souza MB, Medeiros FW, Souza DB, Garcia R, Alves MR. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations. Clinics. 2010;65(12):1223–1228. doi:10.1590/S1807-59322010001200002.
  • Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology. 2012;119(11):2231–2238. doi:10.1016/j.ophtha.2012.06.005.
  • Smadja D, Touboul D, Cohen A, et al. Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol. 2013;156(2):237,246. doi:10.1016/j.ajo.2013.03.034.
  • Cheboli D, Ravindran B Detection of keratoconus by semi-supervised learning. Work-shop on Machine Learning for Health-Care Applications, Helsinki, Finland; 2008.
  • Valdés-Mas MA, Martín-Guerrero JD, Rupérez MJ, et al. A new approach based on machine learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation. Comput Methods Programs Biomed. 2014;116(1):39–47. doi:10.1016/j.cmpb.2014.04.003.
  • Kovács I, Miháltz K, Kránitz K, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg. 2016;42(2):275–283.doi:10.1016/j.jcrs.2015.09.020.
  • Hidalgo IR, Rodriguez P, Rozema JJ, et al. Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography. Cornea. 2016;35(6):827–832. doi:10.1097/ICO.0000000000000834.
  • Issarti I, Rozema J, Consejo A. Corneal modeling and keratoconus identification. Biomath Commun Suppl. 2018;5:1.
  • Libralao GL, de Almedia O, Netto AV, Delbem A, Leon A, de Carvalho F Machine learning techniques for ocular errors analysis. Machine learning for signal processing, 2004. Proceedings of the 14th IEEE Signal Processing Society Workshop; IEEE, Sao Luis, Brazil; 2004.
  • Libralao G, Almeida O, Carvalho A. Classification of ophthalmologic images using an ensemble of classifiers. Innov Appl Artif Intell. 2005;6–13. doi:10.1007/11504894_5.
  • Ohlendorf A, Leube A, Leibig C, Wahl S. A machine learning approach to determine refractive errors of the eye. Invest Ophthalmol Vis Sci. 2017;58:1136.
  • Zhang W, Hasegawa A, Itoh K, Ichioka Y. Image processing of human corneal endothelium based on a learning network. Appl Opt. 1991;30(29):4211–4217. doi:10.1364/AO.30.004211.
  • Hasegawa A, Itoh K, Ichioka Y. Generalization of shift invariant neural networks: image processing of corneal endothelium. Neural Networks. 1996;9(2):345–356. doi:10.1016/0893-6080(95)00054-2.
  • Sharif MS, Qahwaji R, Ipson S, Brahma A. Medical image classification based on artificial intelligence approaches: a practical study on normal and abnormal confocal corneal images. Appl Soft Comput. 2015;36:269–282. doi:10.1016/j.asoc.2015.07.019.
  • Silverman RH, Urs R, RoyChoudhury A, Archer TJ, Gobbe M, Reinstein DZ. Epithelial remodeling as basis for machine-based identification of keratoconus. Invest Ophthalmol Vis Sci. 2014;55(3):1580–1587. doi:10.1167/iovs.13-12578.
  • Koh YW, Celik T, Lee HK, Petznick A, Tong L. Detection of meibomian glands and classification of meibography images. J Biomed Opt. 2012;17(8):0860081. doi:10.1117/1.JBO.17.8.086008.
  • Remeseiro B, Penas M, Mosquera A, Novo J, Penedo M, Yebra-Pimentel E. Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification. Comput Math Methods Med. 2012;2012:1–10. doi:10.1155/2012/207315.
  • Ramos L, Penas M, Remeseiro B, Mosquera A, Barreira N, Yebra-Pimentel E. Texture and color analysis for the automatic classification of the eye lipid layer. Adv Comput Intell. 2011;66–73. doi:10.1007/978-3-642-21498-1_9.
  • Chylack LT, Wolfe JK, Singer DM, et al. The lens opacities classification system III. Arch Ophthalmol. 1993;111(6):831–836. doi:10.1001/archopht.1993.01090060119035.
  • Cheung CY, Li H, Lamoureux EL, et al. Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs. Invest Ophthalmol Vis Sci. 2011;52(3):1314–1319. doi:10.1167/iovs.10-5427.
  • Xu Y, Gao X, Lin S, et al. Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. International Conference on Medical Image Computing and Computer-Assisted Intervention; Springer, Nagoya, Japan; 2013.
  • Bagheri A, Adorno DP, Rizzo P, Barraco R, Bellomonte L. Empirical mode decomposition and neural network for the classification of electroretinographic data. Med Biol Eng Comput. 2014;52(7):619–628. doi:10.1007/s11517-014-1164-8.
  • Garcia-Martin E, Herrero R, Bambo MP, et al. Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis. Semin Ophthalmol. 2015;30(1):11–19. doi:10.3109/08820538.2013.810277.
  • Harangi B, Csordás T, Hajdu A Detecting the excessive activation of the ciliaris muscle on thermal images. IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Smolenice, Slovakia; 2011.
  • Rello L, Ballesteros M Detecting readers with dyslexia using machine learning with eye tracking measures. Proceedings of the 12th Web for All Conference; ACM, Florence, Italy; 2015.
  • Proença H, Filipe S, Santos R, Oliveira J, Alexandre LA. The ubiris. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell. 2010;32(8):1529–1535. doi:10.1109/TPAMI.2009.66.
  • Proenca H. Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans Pattern Anal Mach Intell. 2010;32(8):1502–1516. doi:10.1109/TPAMI.2009.140.
  • Witten IH, Frank E, Hall MA, Pal CJ. Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann publishers, USA; 2016
  • Consejo A, Bartuzel MM, Iskander DR. Corneo-scleral limbal changes following short-term soft contact lens wear. Cont Lens Anterior Eye. 2017;40(5):293–300. doi:10.1016/j.clae.2017.04.007.
  • Consejo A, Behaegel J, Van Hoey M, Wolffsohn JS, Rozema JJ, Iskander DR. Anterior eye surface changes following miniscleral contact lens wear. Contact Lens Anterior Eye. inpress. doi:10.1016/j.clae.2018.06.005.
  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsl. 2009;11(1):10–18. doi:10.1145/1656274.1656278.
  • Segaran T. Programming Collective Intelligence: Building Smart Web 2.0 Applications. O’Reilly Media, USA; 2007
  • Dutton DM, Conroy GV. A review of machine learning. Knowl Eng Rev. 1997;12(4):341–367. doi:10.1017/S026988899700101X.
  • Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, USA; 2009.
  • Bishop CM. Neural Networks for Pattern Recognition. Oxford university press, UK; 1995
  • Murthy SK. Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov. 1998;2(4):345–389. doi:10.1023/A:1009744630224.
  • Fürnkranz J. Separate-and-conquer rule learning. Artif Intell Rev. 1999;13(1):3–54. doi:10.1023/A:1006524209794.
  • Wettschereck D, Aha DW, Mohri T. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell Rev. 1997;11(1/5):273–314. doi:10.1023/A:1006593614256.
  • Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press New York, USA; 1999

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.