Diabetes

Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model.

The World Health Organization (WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, and macrovascular problems. The non-invasive methods like machine learning are reliable and efficient ...

Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy.

AIMS: To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning-based lesion detection and stage grading.

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.

Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce t...

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. There...

Screening of a novel free fatty acid receptor 1 (FFAR1) agonist peptide by phage display and machine learning based-amino acid substitution.

Free fatty acid receptor 1 (FFAR1 or GPR40) has attracted attention for the treatment of type 2 diabetes mellitus, and various small-molecule agonists...

The effect of diabetes on major robotic hepatectomy.

Studies regarding the influence of diabetes on perioperative outcomes after major hepatectomy are conflicting. The objective of this study is to analy...

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR ...

Key factors in a rigorous longitudinal image-based assessment of retinopathy of prematurity.

To describe a database of longitudinally graded telemedicine retinal images to be used as a comparator for future studies assessing grader recall bias...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta caus...

Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes.

AIMS: The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and vali...

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mi...

A review on current advances in machine learning based diabetes prediction.

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it proper...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and pred...

Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort.

BACKGROUND: Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) bas...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

BACKGROUND: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contri...

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into gro...

The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.

Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase paramet...

Diabetes detection using deep learning techniques with oversampling and feature augmentation.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of d...

Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy.

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer visi...