ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2019
Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Integrated transcriptome profiling of plasma exosomes reveals molecular stratification of exocrine and endocrine disorders and S100A8-mediated cell interactions in chronic pancreatitis.Cell discovery · 2025Article
- An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.Scientific reports · 2024Article
- Prediction models for retinopathy of prematurity occurrence based on artificial neural network.BMC ophthalmology · 2024Article
- Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.Scientific reports · 2024Article
- Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.Diagnostics (Basel, Switzerland) · 2023Article
- Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients.Scientific reports · 2023Article
- A Nomogram for Predicting Vision-Threatening Diabetic Retinopathy Among Mild Diabetic Retinopathy Patients: A Case-Control and Prospective Study of Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population.Scientific reports · 2022Article
- Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice.Cureus · 2022Review
- Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study.Frontiers in medicine · 2021Article
- Using Metabolic and Biochemical Indicators to Predict Diabetic Retinopathy by Back-Propagation Artificial Neural Network.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
- Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMonitoring and prediction of diabetic retinopathy (DR) is necessary in patients with diabetes for early discovery and timely treatment of disease. We aimed to analyze the association between DR and biochemical and metabolic parameters, and develop a predictive model for DR.
methodsA total of 530 Chinese residents including 423 with type 2 diabetes (T2D) aged 18 years or older participated in this study. The association between DR and biochemical and metabolic parameters was analyzed by the univariate and multivariable logistic regression (MLR). According to the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tan-sigmoid as the transfer function of the hidden layers nodes, and pure-line of the output layer nodes, with training goal of 0.5×10
resultsThere were 51 (9.6%) diabetic participants with DR. After univariate and MLR analysis, duration of diabetes, waist to hip ratio, HbA
conclusionOur evaluation demonstrated the potential role of BP-ANN model to identify DR in screening practice. The presence of DR was well predictable using the proposed BP-ANN model based on four related parameters (duration of diabetes, waist to hip ratio, HbA
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