首页|Department of Ophthalmology Reports Findings in Diabetic Retinopathy (The applic ation and clinical translation of the selfevolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation)
Department of Ophthalmology Reports Findings in Diabetic Retinopathy (The applic ation and clinical translation of the selfevolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nutritional and Metabo lic Diseases and Conditions - Diabetic Retinopathy is the subject of a report. A ccording to news reporting originating in Ganzhou, People’s Republic of China, b y NewsRx journalists, research stated, “This study aims to analyze the applicati on and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes. A retrospe ctive study was conducted on 300 diabetic patients admitted to our hospital betw een January 2022 and October 2023.” The news reporters obtained a quote from the research from the Department of Oph thalmology, “The patients were divided into a diabetic retinopathy group (n=150) and a non-diabetic retinopathy group (n=150). The improved Beetle Antennae Sear ch (IBAS) was used for hyperparameter optimization in machine learning, and a se lf-evolving machine learning model based on XGBoost was developed. Value analysi s was performed on the predictive features for diabetic retinopathy selected thr ough multifactor logistic regression analysis, followed by the construction of a visualization system to calculate the risk of diabetic retinopathy occurrence. Multifactor logistic regression analysis revealed that being male, having a long er disease duration, higher systolic blood pressure, fasting blood glucose, glyc osylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-c reatinine ratio were risk factors for the development of diabetic retinopathy, w hile non-pharmacological treatment was a protective factor. The self-evolving ma chine learning model demonstrated significant performance advantages in early di agnosis and prediction of diabetic retinopathy occurrence.”
GanzhouPeople’s Republic of ChinaAsi aCyborgsDiabetic AngiopathiesDiabetic RetinopathyEmerging TechnologiesEye Diseases and ConditionsHealth and MedicineMachine LearningNutritional and Metabolic Diseases and ConditionsOphthalmologyRetinal Diseases and Condi tionsRetinopathyRisk and PreventionVascular Diseases and Conditions
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