Learning ensemble classifiers for diabetic retinopathy assessment

IDF, 2016, International diabetes foundation IDF, 2015, International diabetes federation Alghadyan, 2011, Diabetic retinopathy – an update, Saudi J Ophthalmol, 25, 99, 10.1016/j.sjopt.2011.01.009 IDF, 2013, International diabetes federation Control, 1993, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N Engl J Med, 1993, 977 Bourne, 2013, Causes of vision loss worldwide, 1990–2010: a systematic analysis, Lancet Glob Health, 1, e339, 10.1016/S2214-109X(13)70113-X Zhang, 2011, Inflammation and diabetic retinal microvascular complications, J Cardiovas Dis Res, 2, 96, 10.4103/0975-3583.83035 Romero Aroca, 2012, Resultados de la implantación de la cámara no midriática sobre la población diabética, Salud (i) Cienc, 19, 214 ADA, 2015, American Diabetes Association. Standards of medical care in diabetes. Microvascular complications and foot care, Diabetes Care, 38, 858 RCO, 2012 Olafsdottir, 2007, Biennial eye screening in patients with diabetes without retinopathy: 10-year experience, Br J Ophthalmol, 91, 1599, 10.1136/bjo.2007.123810 Chalk, 2012, Can the retinal screening interval be safely increased to 2 years for type 2 diabetic patients without retinopathy?, Diabetes Care, 35, 1663, 10.2337/dc11-2282 Romero-Aroca, 2016, Changes observed in diabetic retinopathy: eight-year follow-up of a Spanish population, Br J Ophthalmol, 100, 1366, 10.1136/bjophthalmol-2015-307689 Saleh, 2012, An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection, Comput Methods Progr Biomed, 108, 186, 10.1016/j.cmpb.2012.03.004 Early Treatment Diabetic Retinopathy Study Research Group, 1991, Grading diabetic retinopathy from stereoscopic color fundus photographsan extension of the modified airlie house classification: {ETDRS} report number 10, Ophthalmology, 98, 786, 10.1016/S0161-6420(13)38012-9 Popescu, 2015, Computer-aided localization of the optic disc based on textural features, 307 Escorcia-Gutiérrez, 2016, Interactive optic disk segmentation via discrete convexity shape knowledge using high-order functionals, 39 Pratt, 2016, Convolutional neural networks for diabetic retinopathy, Proc Comput Sci, 90, 200, 10.1016/j.procs.2016.07.014 Haloi, 2015 Chandrakumar, 2016, Classifying diabetic retinopathy using deep learning architecture, Int J Eng Res Technol, 5 La Torre, 2016, Diabetic retinopathy detection hrough image analysis using deep convolutional neural networks, 58 Wang, 2015, Hierarchical retinal blood vessel segmentation based on feature and ensemble learning, Neurocomputing, 149, 708, 10.1016/j.neucom.2014.07.059 Sanromà, 2016, Assessment of diabetic retinopathy risk with random forests, 313 Skevofilakas, 2010, A hybrid decision support system for the risk assessment of retinopathy development as a long term complication of type 1 diabetes mellitus, 6713 Breiman, 2001, Random forests, Mach Learn, 45, 5, 10.1023/A:1010933404324 Kulkarni, 2013, Random forest classifiers: a survey and future research directions, Int J Adv Comput, 36, 1144 Kosko, 1986, Fuzzy entropy and conditioning, Inf Sci, 40, 165, 10.1016/0020-0255(86)90006-X Wang, 2001, A comparative study on heuristic algorithms for generating fuzzy decision trees, IEEE Trans Syst Man Cybern B: Cybern, 31, 215, 10.1109/3477.915344 Yuan, 1995, Induction of fuzzy decision trees, Fuzzy Sets Syst, 69, 125, 10.1016/0165-0114(94)00229-Z Alickovic, 2016, Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier, J Med Syst, 40, 1, 10.1007/s10916-016-0467-8 Koley, 2016, Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest, Appl Soft Comput, 41, 453, 10.1016/j.asoc.2016.01.022 Kovacs, 2016, Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification, 978507 Cadenas, 2013, Feature subset selection filter-wrapper based on low quality data, Expert Syst Appl, 40, 6241, 10.1016/j.eswa.2013.05.051 Jiang, 2016, Privacy-protected facial biometric verification using fuzzy forest learning, IEEE Trans Fuzzy Syst, 24, 779, 10.1109/TFUZZ.2015.2486803 Zhang, 2012, An ontology-based multi-class terrain surface classification system for aerial imagery, 95 Trawiński, 2013, A multiclassifier approach for topology-based WiFi indoor localization, Soft Comput, 17, 1817, 10.1007/s00500-013-1019-5 Marsala, 2009, A fuzzy decision tree based approach to characterize medical data, 1332 Cadenas, 2016, Gene priorization for tumor classification using an embedded method, 363 Lukmanto, 2015, The early detection of diabetes mellitus (dm) using fuzzy hierarchical model, Proc Comput Sci, 59, 312, 10.1016/j.procs.2015.07.571 Beloufa, 2013, Design of fuzzy classifier for diabetes disease using modified artificial bee colony algorithm, Comput Methods Progr Biomed, 112, 92, 10.1016/j.cmpb.2013.07.009 Meza-Palacios, 2017, Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2 diabetes mellitus, Expert Syst Appl, 72, 335, 10.1016/j.eswa.2016.10.053 Greco, 2001, Rough sets theory for multicriteria decision analysis, Eur J Oper Res, 129, 1, 10.1016/S0377-2217(00)00167-3 Słowiński, 2014, Rough-set-based decision support, 557 Słowiński, 2015, Rough set methodology for decision aiding, 349 Pawlak, 1991 Yao, 2015, Probabilistic rough sets, 387 Napierala, 2016, Types of minority class examples and their influence on learning classifiers from imbalanced data, J Intell Inf Syst, 46, 563, 10.1007/s10844-015-0368-1 Błaszczyński, 2015, Neighbourhood sampling in bagging for imbalanced data, Neurocomputing, 150, 529, 10.1016/j.neucom.2014.07.064 Breiman, 1996, Bagging predictors, Mach Learn, 24, 123, 10.1007/BF00058655 Pałkowski, 2014, Application of rough set theory to prediction of antimicrobial activity of bis-quaternary imidazolium chlorides, Fundam Inf, 132, 315, 10.3233/FI-2014-1046 Pałkowski, 2014, Antimicrobial activity and sar study of new gemini imidazolium-based chlorides, Chem Biol Drug Des, 83, 278, 10.1111/cbdd.12236 Pałkowski, 2015, Prediction of antifungal activity of gemini imidazolium compounds, BioMed Res Int, 2015 Gardiner, 2015, Perspectives on knowledge discovery algorithms recently introduced in chemoinformatics: rough set theory, association rule mining, emerging patterns, and formal concept analysis, J Chem Inf Model, 55, 1781, 10.1021/acs.jcim.5b00198 Blasco, 2015, Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis, J Biomed Inf, 53, 291, 10.1016/j.jbi.2014.12.001 Cinelli, 2015, A green chemistry-based classification model for the synthesis of silver nanoparticles, Green Chem, 17, 2825, 10.1039/C4GC02088J Khan, 2004, Data mining the PIMA dataset using rough set theory with a special emphasis on rule reduction, Multitopic conference, 2004. Proceedings of INMIC 2004. 8th international, 334, 10.1109/INMIC.2004.1492899 Stepaniuk, 1999, Rough set data mining of diabetes data, Found Intell Syst, 457, 10.1007/BFb0095133 Su, 2006, Data mining for the diagnosis of type ii diabetes from three-dimensional body surface anthropometrical scanning data, Comput Math Appl, 51, 1075, 10.1016/j.camwa.2005.08.034 Nakayama, 1999, Rule extraction based on rough set theory and its application to medical data analysis, 924 Wilkinson, 2003, Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales, Ophthalmology, 110, 1677, 10.1016/S0161-6420(03)00475-5 Cadenas, 2012, Extending information processing in a fuzzy random forest ensemble, Soft Comput, 16, 845, 10.1007/s00500-011-0777-1 Błaszczyński, 2012, Inductive discovery of laws using monotonic rules, Eng Appl Artif Intell, 25, 284, 10.1016/j.engappai.2011.09.003 Błaszczyński, 2011, Sequential covering rule induction algorithm for variable consistency rough set approaches, Inf Sci, 181, 987, 10.1016/j.ins.2010.10.030 Greco, 2016, Measures of rule interestingness in various perspectives of confirmation, Inf Sci, 346, 216, 10.1016/j.ins.2016.01.056 Błaszczyński, 2009, Monotonic variable consistency rough set approaches, Int J Approx Reason, 50, 979, 10.1016/j.ijar.2009.02.011 Saleh, 2016, Diabetic retinopathy risk estimation using fuzzy rules on electronic health record data, 263 Cvetković, 2016, Activity recognition for diabetic patients using a smartphone, J Med Syst, 40, 256, 10.1007/s10916-016-0598-y Kafali, 2014, Hydra: a hybrid diagnosis and monitoring architecture for diabetes, 2014 IEEE 16th international conference on e-health networking, applications and services (healthcom), 531, 10.1109/HealthCom.2014.7001898 Błaszczyński, 2011, Rule-based estimation of attribute relevance, 36