Abstract
Diabetic retinopathy (DR) is one of the complications of diabetes mellitus, which is an important manifestation of diabetic microangiopathy and major cause of vision loss in middle-aged and elderly people worldwide. Establishing a risk prediction model for diabetic retinopathy can discover high-risk groups and early warn diabetic retinopathy, which can effectively reduce the medical cost of diabetes. The experimental data was derived from the electronic medical records of a tertiary hospital of Beijing from 2013 to 2017, including 29 inspection indicators. In this study, we compared the predictive models of type 2 diabetes mellitus complicated with retinopathy, and finally selected the random forest method to construct the risk prediction model. The weights of each index are analyzed by linear regression algorithm, the combination of inspection indicators with the highest accuracy is selected, and the random forest model is optimized to improve the accuracy of the classification prediction model, accuracy increased by 3.7264%. The predictive model provides a basis for early diagnosis of diabetic retina and optimization of the diagnostic process.
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This work is supported by the CERNET Innovation Project (No. NGII20170719) and the Beijing Municipal Education Commission.
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Developed the method: JY, XH and YY. Conceived and designed the experiments: XD. Analyzed the data: XD and JY. Wrote the first draft of the manuscript: XD. Contributed to the writing of the manuscript: XD, JY, XH and YY. Agree with the manuscript results and conclusions: JY, YY and XD Jointly developed the structure and arguments for the paper: JY, YY and XD. Made critical revisions and approved final version: JY, XD and YY. All the authors reviewed and approved of the final manuscript.
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Yan, J., Du, X., Yu, Y., Xu, H. (2019). Establishment of Risk Prediction Model for Retinopathy in Type 2 Diabetic Patients. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_23
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DOI: https://doi.org/10.1007/978-3-030-37078-7_23
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