التفاصيل البيبلوغرافية
| العنوان: |
Table_1_Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.doc |
| المؤلفون: |
Xiao zhu Liu, Minjie Duan, Hao dong Huang, Yang Zhang, Tian yu Xiang, Wu ceng Niu, Bei Zhou, Hao lin Wang, Ting ting Zhang |
| سنة النشر: |
2023 |
| المجموعة: |
Frontiers: Figshare |
| مصطلحات موضوعية: |
Endocrinology, Reproduction, Cell Metabolism, type 2 diabetes mellitus, diabetic kidney disease, machine learning, prediction, CatBoost model |
| الوصف: |
Objective Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms. Methods Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings. Results DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C). Conclusion A machine learning-based model for the prediction of the risk of developing ... |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/Table_1_Predicting_diabetic_kidney_disease_for_type_2_diabetes_mellitus_by_machine_learning_in_the_real_world_a_multicenter_retrospective_study_doc/23622273 |
| DOI: |
10.3389/fendo.2023.1184190.s001 |
| الاتاحة: |
https://doi.org/10.3389/fendo.2023.1184190.s001
https://figshare.com/articles/dataset/Table_1_Predicting_diabetic_kidney_disease_for_type_2_diabetes_mellitus_by_machine_learning_in_the_real_world_a_multicenter_retrospective_study_doc/23622273 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.9A816B0F |
| قاعدة البيانات: |
BASE |