Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
The exponential increase in the number of diabetics around the world has led to an equally
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …
[HTML][HTML] Applications of artificial intelligence to electronic health record data in ophthalmology
Widespread adoption of electronic health records (EHRs) has resulted in the collection of
massive amounts of clinical data. In ophthalmology in particular, the volume range of data …
massive amounts of clinical data. In ophthalmology in particular, the volume range of data …
Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models
Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause
damage from mild vision problems to complete blindness. It has been observed that the eye …
damage from mild vision problems to complete blindness. It has been observed that the eye …
Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images
Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of
diabetes. DR has become a severe health concern worldwide, as the number of diabetes …
diabetes. DR has become a severe health concern worldwide, as the number of diabetes …
A data-driven approach to referable diabetic retinopathy detection
Prior art on automated screening of diabetic retinopathy and direct referral decision shows
promising performance; yet most methods build upon complex hand-crafted features whose …
promising performance; yet most methods build upon complex hand-crafted features whose …
Auto loan fraud detection using dominance-based rough set approach versus machine learning methods
Financial fraud is escalating as financial services and operations grow. Despite preventive
actions and security measures deployed to mitigate financial fraud, fraudsters are learning …
actions and security measures deployed to mitigate financial fraud, fraudsters are learning …
Artificial intelligence in clinical decision support: a focused literature survey
Objectives: This survey analyses the latest literature contributions to clinical decision support
systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt …
systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt …
Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus
Diabetes mellitus (DM) is a combination of metabolic disorders characterized by elevated
blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of …
blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of …
[HTML][HTML] Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors
J Pascual-Fontanilles, A Valls… - Knowledge-Based Systems, 2024 - Elsevier
In this paper, we explore the possibility of exploiting retrospective clinical data from
Electronic Health Records (EHR) for classification tasks in chronic patients. The different …
Electronic Health Records (EHR) for classification tasks in chronic patients. The different …
Use of machine learning approaches in clinical epidemiological research of diabetes
Abstract Purpose of Review Machine learning approaches—which seek to predict outcomes
or classify patient features by recognizing patterns in large datasets—are increasingly …
or classify patient features by recognizing patterns in large datasets—are increasingly …