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Blood hypercoagulation state lower limb deep vein thrombosis construction and validation of an early warning model based on random forest algorithm in diabetic peripheral neuropathy

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Author:
No author available
Journal Title:
Chinese Journal of Diabetes
Issue:
8
DOI:
10.3969/j.issn.1006-6187.2024.08.006
Key Word:
随机森林算法;糖尿病周围神经病变;预测模型构建;验证;Random forest algorithm;Diabetic peripheral neuropathy;Early warning model construction;Validation

Abstract: Objective To explore the influencing factors of type 2 diabetes mellitus(T2DM)merging with diabetic peripheral neuropathy(DPN),and to construct and verify a prediction model based on random forest algorithm.Methods 512 T2DM patients who were hospitalized in our hospital from January 2019 to December 2021 were divided into simple T2DM group(n=292)and T2DM combined with DPN group(DPN,n=220)based on whether or not DPN was present.The general data and biochemical indicators of the two groups were compared.Logistic regression analysis was conducted to identify the influencing factors of DPN in T2DM patients.A random forest model was constructed.Results Compared with the T2DM group,the DPN group showed an increase in weight loss rate,incidence of diabetic retinopathy(DR),WBC and HbA1c(P<0.05),with decrease in DM duration≥10 years,TG and HDL-C(P<0.05).Logistic regression analysis showed that age≥60 years,HbA1c,TG,HDL-C,rate of weight loss,DR were influencing factor for T2DM combined with DPN.The random forest model showed that when the number of trees was 387,the error rate was the lowest.The importance ranking of the influencing factors of T2DM combined with DPN were the rate of weight loss,TG,DR,HDL-C,HbA1c and age≥60 years.Conclusions Age≥60 years,HbA1c,TG,HDL-C,rate of weight loss and DR are influencing factors for T2DM combined with DPN,that can be used for early clinical diagnosis and treatment.

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