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Diabetes Mellitus

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Deep learning-based hemorrhage detection for diabetic retinopathy screening.

Scientific reports
Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabe...

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Scientific reports
Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic ret...

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology.

Frontiers in public health
BACKGROUND: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, ...

Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods.

Diabetes & metabolic syndrome
BACKGROUND & AIM: Diabetes mellitus has become one of the out brakes causing major health issues in developing countries like India. The need for leveraging technology is felt in diabetes management. The main objective of this work is to deploy machi...

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Frontiers in endocrinology
BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for ...

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.

Journal of nursing management
AIM: The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.

International journal of environmental research and public health
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning ...

Design and Usability of an Avatar-Based Learning Program to Support Diabetes Education: Quality Improvement Study in Colombia.

Journal of diabetes science and technology
BACKGROUND: This quality improvement study, entitled Avatar-Based LEarning for Diabetes Optimal Control (ABLEDOC), explored the feasibility of delivering an educational program to people with diabetes in Colombia. The aim was to discover how this app...

A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction.

Computers in biology and medicine
As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of...

An automated unsupervised deep learning-based approach for diabetic retinopathy detection.

Medical & biological engineering & computing
As per the International Diabetes Federation (IDF) report, 35-60% of people suffering from diabetic retinopathy (DR) have a history of diabetes. DR is one of the primary reasons for blindness and visual impairment worldwide among adults aged 24-74 ye...