Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study.

ABSTRACT:

Background

Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems.

Methods

For the purposes of our study, we developed a s

SUBMITTER: Metsker O 

PROVIDER: S-EPMC7444272 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature