Intelligent classification of hypoglycemia treatment plan for patients with type 2 diabetes based on ensemble learning

  Objective  To apply Adaboost in the determination of hypoglycemia treatment plan in patients with type 2 diabetes.

  Methods  Clinical data about 3 005 patients with type 2 diabetes hospitalized in the first medical center of Chinese PLA General Hospital from 2013 to 2017 were collected, including medical prescriptions, biochemical testing results, clinical manifestations, demographic characteristics, etc. Adaboost algorithm was used to establish the machine learning model and classify the treatment plan of the patients, with 1 697 cases as training set and 1 308 cases as testing set randomly, and then accuracy and Kappa coefficient of the model were computed.

  Results  The prediction accuracy of the model by Adaboost was 64.2% and the Kappa coefficient was 0.36. After analyzing the model established by Adaboost, we found that UCr, HbA1c, CK-MB, FBG, etc. were significantly related to the treatment plan selecting.

  Conclusion  To some extent, Adaboost algorithm is feasible and accurate in predicting hypoglycemia treatment plan.