Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 - 84.7 % for the conditional diagnosis of DPN in primary care.

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

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.

Risk Factors for Chronic Diabetes Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS), can reach up to AUC 65.8-84.7 % for the conditional diagnosis of DPN in primary care.