Application of novel subgroups of Chinese inpatients with diabetes based on machine learning paradigm

Six variables were used to determine five diabetes subgroups in European, Chinese and US populations in previous studies. This study aims to make new classification method of diabetes easier to use in clinical settings. Clinical data of 1152 hospitalized diabetic patients were collected and built a highly accurate model based on machine learning paradigm. We visualized the confusion matrix of the classification model. The diagnose accuracy of five clusters (MOD, MARD, SIRD, SIDD and SAID) were 95%, 100%, 99%, 96% and 100%. An online tool (uqzhichen.uqcloud.net) was set up according to the cluster data based on machine learning paradigm. Six variables (age when diagnosed, HbA1c, BMI, HOMA2-β, HOMA2-IR and GADA) were needed to input in this diagnose system and then a highly accurate subgroup result was showed. This is a stable and accurate online diagnose system to identify five new subgroups of diabetes based on machine learning paradigm.