Open AccessA Novel Classification Indicator of Type 1 and Type 2 Diabetes in China
Author(s) -
Yannian Wang,
Shanshan Liu,
Ruoxi Chen,
Zhongning Chen,
Yuan Jin-lei,
Quanzhong Li
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/s41598-017-17433-8
Subject(s) - type 2 diabetes , diabetes mellitus , medicine , artificial intelligence , guideline , mathematics , classification scheme , adaboost , type (biology) , classifier (uml) , pattern recognition (psychology) , computer science , statistics , machine learning , endocrinology , pathology , biology , ecology
Because of the differences of treatment, it is extremely important to classify the types of diabetes, especially for the diagnosis made by clinician. In this study, we proposed a novel scheme calculating an indicator of classifying diabetes, which contains two stages: the first is a model of feature extraction, 17 features are automatically extracted from the curve of glucose concentration acquired by continuous glucose monitoring system (CGM); the second is a model of diabetes parameter regression based on an ensemble learning algorithm named double-Class AdaBoost. 1050 curves of glucose concentration of type 1 and type 2 diabetics were acquired at the Department of Endocrinology in People’s Hospital of Zhengzhou University China, and an upper threshold μ was set to 7 mmol/L, 8 mmol/L, 9 mmol/L, 10 mmo/L, and 11 mmol/L respectively according to the guideline of WHO. The experiments show that the coincidence rate of our scheme and clinical diagnosis is 90.3%. The novel indicator extends the criteria in diagnosing types of diabetes and provides doctors with a scalar to classify diabetes of type 1 and type 2.