… , the classification as well as prediction accuracy is not so high. So this paper proposes a
model centered on Deep Learning (DL) for predicting complications of Type 2 Diabetes Mellitus…

… approach has been increasingly used for clinical risk prediction and classification. It is
anticipated that predictive modelling using data from EHRs will drive personalised medicine …

… This paper introduced a state-of-the-art deep learning model for the classification of …
classification of DR, in the near future, this research will be helpful to design a general deep learning …

… [35] developed a hybrid fuzzy deep learning approach for the detection of diabetes. Firstly,
the data was fuzzified. After that, a 5 × 5 fuzzy matrix was constructed where columns …

… to develop deep learning based Deep Belief Network intelligent model predict diabetes. This
… In this section, the experimental setup for classification of diabetes implemented using DBN …

… study is to develop a machine learning (ML)-based system for predicting diabetic patients. …
and classification of diabetes patients while there are 6561 respondents with 657 diabetics …

… Deep learning algorithms have been used to detect diabetic … retinal images of patients
with diabetes from the community-… for DR severity and referable diabetic macular edema (DME). …

… algorithms for various medical image classification tasks, 11 -13 including retinal imaging.
Specifically for DR, multiple groups have shown that deep learning can be leveraged to …

… the deep learning-based ensemble approach for diabetic … Automatic detection and
classification of diabetic retinopathy … Classification of diabetic retinopathy images by using deep …

… ), and the second “5” to the 5 time points (hence, multi-label classification) at which we
observed each of the five outcomes, for a total of 25 reference values per subject. …