TY  - JOUR
TI  - A Review on Prediction of Diabetic Retinopathy Using Data Mining Algorithms
AU  - Kajal Sanjay Kothare
AU  -  Prof. Kalpana Malpe
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2019/02/28
PY  - 2019
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT195179
VL  - 5
IS  - 1
SP  - 266	
EP  - 272
AB  - The risking components of diabetic retinopathy (DR) were examined broadly in the past investigations, yet it stays obscure which chance variables were more connected with the DR than others. On the off chance that we can distinguish the DR related hazard factors all the more precisely, we would then be able to practice early avoidance systems for diabetic retinopathy in the most high-chance populace. The motivation behind this examination to study and consider the different predicting mechanisms for the DR in diabetes mellitus utilizing data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.