Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy

Author:

Oladele Tinuke O.,Ogundokun Roseline Oluwaseun,Kayode Aderonke Anthonia,Adegun Adekanmi Adeyinka,Adebiyi Marion Oluwabunmi

Publisher

Springer International Publishing

Reference10 articles.

1. Aravind, C., PonniBala, M., Vijaychitra, S.: Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM Technique. In: International Conference on Innovations in Intelligent Instrumentation, Optimization and Signal Processing, pp. 18–22 (2013)

2. Antal, B., Hajdu, A.: An ensemble-based system for automatic screening of diabetic retinopathy. Knowl.-Based Syst. 60, 20–27 (2014)

3. Bhaisare, A., Lachure, S., Bhagat, A., Lachure, J.: Diabetic retinopathy diagnosis using image mining. Int. Res. J. Eng. Technol. 3(10), 858–861 (2016)

4. Elibol, G., Ergin, S.: The assessment of time-domain features for detecting symptoms of diabetic retinopathy. Int. J. Intell. Syst. Appl. Eng. 4(Special Issue), 136–140 (2016)

5. Evirgen, H., Çerkezi, M.: Prediction and diagnosis of diabetic retinopathy using data mining technique. Online J. Sci. Technol. 4(3), 32–37 (2004)

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