A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach

New Markers for the Assessment of Microvascular Complications in Patients with Metabolic Syndrome

Background: Metabolic syndrome is a complex disorder characterized by the coexistence of multiple risk factors, including dysglycemia, hypertension, dyslipidemia, and visceral obesity. Both metabolic syndrome and diabetes mellitus are closely associated with the onset of microvascular complications such as retinopathy, polyneuropathy, and nephropathy. Methods: This narrative review analyzed 137 studies published up to 2025, retrieved from PubMed and Crossref databases. The objective was to identify and evaluate potential biomarkers that could facilitate the early detection of microvascular complications in patients with metabolic syndrome. Results: Several biomarkers demonstrated a strong correlation with microvascular complications in individuals with metabolic syndrome. These findings suggest their potential role in early diagnosis and risk assessment. Conclusions: The identification of reliable biomarkers may enhance early detection and targeted interventions for microvascular complications in metabolic syndrome. Further research is essential to validate these markers and establish their clinical applicability in routine medical practice.

A Robust Machine Learning Model for Diabetic Retinopathy Classification

Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.