  Diabetic Retinopathy (DR) is among the worlds leading vision loss causes in
diabetic patients. DR is a microvascular disease that affects the eye retina,
which causes vessel blockage and therefore cuts the main source of nutrition
for the retina tissues. Treatment for this visual disorder is most effective
when it is detected in its earliest stages, as severe DR can result in
irreversible blindness. Nonetheless, DR identification requires the expertise
of Ophthalmologists which is often expensive and time-consuming. Therefore,
automatic detection systems were introduced aiming to facilitate the
identification process, making it available globally in a time and
cost-efficient manner. However, due to the limited reliable datasets and
medical records for this particular eye disease, the obtained predictions
accuracies were relatively unsatisfying for eye specialists to rely on them as
diagnostic systems. Thus, we explored an ensemble-based learning strategy,
merging a substantial selection of well-known classification algorithms in one
sophisticated diagnostic model. The proposed framework achieved the highest
accuracy rates among all other common classification algorithms in the area. 4
subdatasets were generated to contain the top 5 and top 10 features of the
Messidor dataset, selected by InfoGainEval. and WrapperSubsetEval., accuracies
of 70.7% and 75.1% were achieved on the InfoGainEval. top 5 and original
dataset respectively. The results imply the impressive performance of the
subdataset, which significantly conduces to a less complex classification
process
