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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Clinical Diabetes
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1429974

The Application and Clinical Translation of the Self-Evolving Machine Learning Methods in Predicting Diabetic Retinopathy and Visualizing Clinical Transformation

Provisionally accepted
Binbin Li Binbin Li 1*Liqun Hu Liqun Hu 1Siqing Zhang Siqing Zhang 2Shaojun Li Shaojun Li 1Wei Tang Wei Tang 1Guishang Chen Guishang Chen 1
  • 1 Department of Ophthalmology, Ganzhou People's Hospital, Ganzhou, China
  • 2 Department of Endocrinology, Ganzhou People's Hospital, Ganzhou, China

The final, formatted version of the article will be published soon.

    Objective:This study aims to analyze the application and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes. Methods: A retrospective study was conducted on 300 diabetic patients admitted to our hospital between January 2022 and October 2023. The patients were divided into a diabetic retinopathy group (n=150) and a non-diabetic retinopathy group (n=150). The improved Beetle Antennae Search (IBAS) was used for hyperparameter optimization in machine learning, and a self-evolving machine learning model based on XGBoost was developed. Value analysis was performed on the predictive features for diabetic retinopathy selected through multifactor logistic regression analysis, followed by the construction of a visualization system to calculate the risk of diabetic retinopathy occurrence. Results:Multifactor logistic regression analysis revealed that being male, having a longer disease duration, higher systolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-creatinine ratio were risk factors for the development of diabetic retinopathy, while non-pharmacological treatment was a protective factor. The self-evolving machine learning model demonstrated significant performance advantages in early diagnosis and prediction of diabetic retinopathy occurrence. Conclusion:The application of the self-evolving machine learning models can assist in identifying features associated with diabetic retinopathy in clinical settings, enabling early prediction of disease occurrence and aiding in the formulation of treatment plans to improve patient prognosis.

    Keywords: Diabetic Retinopathy, Self-evolving machine learning, Diagnostic prediction, Visualizing clinical transformation, artificial intelligence

    Received: 10 May 2024; Accepted: 07 Aug 2024.

    Copyright: © 2024 Li, Hu, Zhang, Li, Tang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Binbin Li, Department of Ophthalmology, Ganzhou People's Hospital, Ganzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.