… advances in deep learning 1,2 , particularly in the case of deep convolutional neural … diabetes
screening programmes and biobanks, cardiovascular risk factors, the presence of diabetic …

… efficiently diagnoses diabetic retinopathy. Comparing the proposed activation with traditional
deep learning models, we found that it improved diagnosis and classification performance. …

… of doing research to improve the accuracy of diabetes patient classification. Some recent
works on diabetes prediction using machine learning algorithms are described in this section. …

… a DSS for diabetes prediction based on Machine Learning (ML) … machine learning with deep
learning approaches. For … a deep learningbased method for diabetes data classification by …

… The accuracy obtained through diverse classifiers is shown below by the confusion
matrix which consists of class precision, diabetes prediction yes, diabetes prediction no, …

… The current research paper focuses on the concept of classification of DR … a deep learning
model. This paper proposes a deep learning-based automated detection and classification …

… A stacked autoencoders based Deep Learning framework for classification of Type 2 Diabetes
data is proposed in this paper. This approach is experimented on UCI machine learning …

… class, while the negative class is drawn from a single distribution. For different clusters of the
positive class, … be different and sparse relative to the negative class [17]. Clusters are like “…

… We formulate the problem as a supervised learning task, specifically, a classification
problem. This entailed estimating the conditional probability distribution P(D|I, w) of the label D …

… been proposed by employing deep learning (DL) and quantum machine learning (QML) …
classification of diabetes are available. However, it has been found that the lethality of diabetes …