The application of multivariate adaptive regression splines in exploring the influencing factors and predicting the prevalence of HbA1c improvement | Zendy

Rui Lu | Zendy; Tongqing Duan | Zendy; Mengyang Wang | Zendy; Hongwei Liu | Zendy; Siyuan Feng | Zendy; Xiaowen Gong | Zendy; Wang Hui | Zendy; Jiao Wang | Zendy; Zhuang Cui | Zendy; Yuanyuan Liu | Zendy; Changping Li | Zendy; Jun Ma | Zendy

Open Access

The application of multivariate adaptive regression splines in exploring the influencing factors and predicting the prevalence of HbA1c improvement

Author(s) -

Rui Lu,

Tongqing Duan,

Mengyang Wang,

Hongwei Liu,

Siyuan Feng,

Xiaowen Gong,

Wang Hui,

Jiao Wang,

Zhuang Cui,

Yuanyuan Liu,

Changping Li,

Jun Ma

Publication year - 2020

Publication title -

annals of palliative medicine

Language(s) - English

Resource type - Journals

eISSN - 2224-5839

pISSN - 2224-5820

DOI - 10.21037/apm-19-406

Subject(s) - medicine , multivariate adaptive regression splines , multivariate statistics , multivariate analysis , regression , regression analysis , bayesian multivariate linear regression , statistics , mathematics

Glycosylated hemoglobin (HbA1c) is directly proportional to the level of glucose in the blood, and it has been the gold standard to evaluate the status of long-term blood glucose levels. Exploring the factors that lead to HbA1c improvement is beneficial for effectively controlling of HbA1c levels.

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