表4：机器学习在大型人群研究中识别可转诊糖尿病视网膜病变的实用性

数据集获取（Figshare）

• 源站标题：Table_4_Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study.docx
• DOI：10.3389/fmed.2021.773881.s001
• 发布日期：2021-12-16
• 文件数：1 个
• 文件总体积：20.30 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Usefulness_of_Machine_Learning_for_Identification_of_Referable_Diabetic_Retinopathy_in_a_Large-Scale_Population-Based_Study_docx/17212349

命令行下载

curl -L -o "Table_4_Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study.docx" "https://ndownloader.figshare.com/files/31811825"

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• Table_4_Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study.docx（20.30 KB）

文件总体积 20.30 KB（源站 API 实测）。

数据说明

Purpose: To development and validation of machine learning-based classifiers based on simple non-ocular metrics for detecting referable diabetic retinopathy (RDR) in a large-scale Chinese population–based survey.
Methods: The 1,418 patients with diabetes mellitus from 8,952 rural residents screened in the population-based Dongguan Eye Study were used for model development and validation. Eight algorithms [extreme gradient boosting (XGBoost), random forest, naïve Bayes, k-nearest neighbor (KNN), AdaBoost, Light GBM, artificial neural network (ANN), and logistic regression] were used for modeling to detect RDR in individuals with diabetes. The area under the receiver operating characteristic curve (AUC) and their 95% confidential interval (95% CI) were estimated using five-fold cross-validation as well as an 80:20 ratio of training and validation.
Results: The 10 most important features in machine learning models were duration of diabetes, HbA1c, systolic blood pressure, triglyceride, body mass index, serum creatine, age, educational level, duration of hypertension, and income level. Based on these top 10 variables, the XGBoost model achieved the best discriminative performance, with an AUC of 0.816 (95%CI: 0.812, 0.820). The AUCs for logistic regression, AdaBoost, naïve Bayes, and Random forest were 0.766 (95%CI: 0.756, 0.776), 0.754 (95%CI: 0.744, 0.764), 0.753 (95%CI: 0.743, 0.763), and 0.705 (95%CI: 0.697, 0.713), respectively.
Conclusions: A machine learning–based classifier that used 10 easily obtained non-ocular variables was able to effectively detect RDR patients. The importance scores of the variables provide insight to prevent the occurrence of RDR. Screening RDR with machine learning provides a useful complementary tool for clinical practice in resource-poor areas with limited ophthalmic infrastructure.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.773881.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Usefulness_of_Machine_Learning_for_Identification_of_Referable_Diabetic_Retinopathy_in_a_Large-Scale_Population-Based_Study_docx/17212349）。