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Integration of Different Fuzzy Rule-Induction Methods to Improve the Classification of Patients with Diabetic Retinopathy., , , и . CCIA, том 300 из Frontiers in Artificial Intelligence and Applications, стр. 6-15. IOS Press, (2017)Challenges in the Exploitation of Historical Clinical Data for the Classification of Diabetic Retinopathy Patients., , , и . CCIA, том 375 из Frontiers in Artificial Intelligence and Applications, стр. 204-207. IOS Press, (2023)Iterative Update of a Random Forest Classifier for Diabetic Retinopathy., , , и . CCIA, том 339 из Frontiers in Artificial Intelligence and Applications, стр. 207-216. IOS Press, (2021)Continuous Dynamic Update of Fuzzy Random Forests., , , и . Int. J. Comput. Intell. Syst., 15 (1): 74 (2022)Interactive Optic Disk Segmentation via Discrete Convexity Shape Knowledge Using High-Order Functionals., , , , и . CCIA, том 288 из Frontiers in Artificial Intelligence and Applications, стр. 39-44. IOS Press, (2016)Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors., , и . Knowl. Based Syst., (2024)A Fuzzy Random Forest Approach for the Detection of Diabetic Retinopathy on Electronic Health Record Data., , , , и . CCIA, том 288 из Frontiers in Artificial Intelligence and Applications, стр. 169-174. IOS Press, (2016)Assessment of diabetic retinopathy risk with random forests., , , , , и . ESANN, (2016)Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models., , , , , и . MDAI, том 11144 из Lecture Notes in Computer Science, стр. 114-127. Springer, (2018)Diabetic Retinopathy Risk Estimation Using Fuzzy Rules on Electronic Health Record Data., , , , , и . MDAI, том 9880 из Lecture Notes in Computer Science, стр. 263-274. Springer, (2016)