The Correlation between the Level of Skin Advanced Glycation End Products in Type 2 Diabetes Mellitus and the Stages of Diabetic Retinopathy and the Types of Traditional Chinese Medicine Syndrome

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM

To develop a classifier for traditional Chinese medicine (TCM) syndrome differentiation of diabetic retinopathy (DR), using optimized machine learning algorithms, which can provide the basis for TCM objective and intelligent syndrome differentiation.

METHODS

Collated data on real-world DR cases were collected. A variety of machine learning methods were used to construct TCM syndrome classification model, and the best performance was selected as the basic model. Genetic Algorithm (GA) was used for feature selection to obtain the optimal feature combination. Harris Hawk Optimization (HHO) was used for parameter optimization, and a classification model based on feature selection and parameter optimization was constructed. The performance of the model was compared with other optimization algorithms. The models were evaluated with accuracy, precision, recall, and F1 score as indicators.

RESULTS

Data on 970 cases that met screening requirements were collected. Support Vector Machine (SVM) was the best basic classification model. The accuracy rate of the model was 82.05%, the precision rate was 82.34%, the recall rate was 81.81%, and the F1 value was 81.76%. After GA screening, the optimal feature combination contained 37 feature values, which was consistent with TCM clinical practice. The model based on optimal combination and SVM (GA_SVM) had an accuracy improvement of 1.92% compared to the basic classifier. SVM model based on HHO and GA optimization (HHO_GA_SVM) had the best performance and convergence speed compared with other optimization algorithms. Compared with the basic classification model, the accuracy was improved by 3.51%.

CONCLUSION

HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR. It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Endogenous advanced glycation end products in the pathogenesis of chronic diabetic complications

Diabetes is a common metabolic illness characterized by hyperglycemia and is linked to long-term vascular problems that can impair the kidney, eyes, nerves, and blood vessels. By increasing protein glycation and gradually accumulating advanced glycation end products in the tissues, hyperglycemia plays a significant role in the pathogenesis of diabetic complications. Advanced glycation end products are heterogeneous molecules generated from non-enzymatic interactions of sugars with proteins, lipids, or nucleic acids via the glycation process. Protein glycation and the buildup of advanced glycation end products are important in the etiology of diabetes sequelae such as retinopathy, nephropathy, neuropathy, and atherosclerosis. Their contribution to diabetes complications occurs via a receptor-mediated signaling cascade or direct extracellular matrix destruction. According to recent research, the interaction of advanced glycation end products with their transmembrane receptor results in intracellular signaling, gene expression, the release of pro-inflammatory molecules, and the production of free radicals, all of which contribute to the pathology of diabetes complications. The primary aim of this paper was to discuss the chemical reactions and formation of advanced glycation end products, the interaction of advanced glycation end products with their receptor and downstream signaling cascade, and molecular mechanisms triggered by advanced glycation end products in the pathogenesis of both micro and macrovascular complications of diabetes mellitus.