Diabetes

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multimodal data, i.e., sensor data and self-reported event data, organized as multi-varia...

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) models have been designed to detect diabetes, many exhibit unsatisfactory performance, are not publicly available, and lack validation on external data...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes i...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases lik...

Demystifying Infusion Pumps: Design of a Cost-Effective Platform for Education and Innovation.

INTRODUCTION: This article presents a cost-effective, modular infusion platform to help diabetes specialists customize and understand infusion pump me...

Identification of biomarkers associated with macrophage polarization in diabetic cardiomyopathy based on bioinformatics and machine learning approaches.

BACKGROUND: Numerous studies have investigated the role of macrophages in the pathogenesis of diabetic cardiomyopathy (DCM); however, the underlying m...

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models ...

An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network.

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnos...

[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile].

UNLABELLED: Early detection of diabetic retinopathy is critical for preventing vision loss.

Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.

The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. Identifying these conditions promptly guarantees e...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical to...

A review on retinopathy of prematurity.

BACKGROUND: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. It predominantly affects preterm infants with very low birth w...

Optimal selection of diagnostic method for diabetes mellitus using complex bipolar fuzzy dynamic data.

Diabetes mellitus refers to a collection of metabolic disorders that affect the way carbohydrates are processed in the body. It is a prominent worldwi...

Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.

BACKGROUND: The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 d...

Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.

BACKGROUND: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. I...

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.

OBJECTIVE: To develop and validate a deep learning model for diabetic macular edema (DME) detection using color fundus imaging, which is applicable in...

Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: A modeling study.

AIMS: We evaluated the cost-effectiveness of artificial intelligence (AI)-based diabetic retinopathy (DR) screening in Japan. This evaluation compared...

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesis and progression in special regions. We investiga...

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.

The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine ...

A deep learning based model for diabetic retinopathy grading.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Exist...