Plasma cytokines for predicting diabetic retinopathy among type 2 diabetic patients via machine learning algorithms.

ABSTRACT:

Aims

This study aimed to investigate changes of plasma cytokines and to develop machine learning classifiers for predicting non-proliferative diabetic retinopathy among type 2 diabetes mellitus patients.

Results

There were 12 plasma cytokines significantly higher in the non-proliferative diabetic retinopathy group in the pilot cohort. The validation cohort showed that angiopoietin 1, platelet-derived growth factor-BB, tissue inhibitors of metalloproteinase 2 and vascular endothelial growth factor receptor 2 were significantly higher in the NPDR group. Machine learning algorithms using the random forest yielded the best performance, with sensitivity of 92.3%, specificity of 75%, PPV of 82.8%, NPV of 88.2% and area under the curve of 0.84.

Conclusions

Plasma angiopoieti

SUBMITTER: Cao B 

PROVIDER: S-EPMC7880388 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature