PMID- 31923820
OWN - NLM
STAT- MEDLINE
DCOM- 20210208
LR  - 20210208
IS  - 1872-7565 (Electronic)
IS  - 0169-2607 (Linking)
VI  - 188
DP  - 2020 May
TI  - Analysis for warning factors of type 2 diabetes mellitus complications with 
      Markov blanket based on a Bayesian network model.
PG  - 105302
LID - S0169-2607(19)30633-9 [pii]
LID - 10.1016/j.cmpb.2019.105302 [doi]
AB  - BACKGROUND AND OBJECTIVE: Type 2 diabetes mellitus (T2DM) complications seriously 
      affect the quality of life and could not be cured completely. Actions should be 
      taken for prevention and self-management. Analysis of warning factors is 
      beneficial for patients, on which some previous studies focused. They generally 
      used the professional medical test factors or complete factors to predict and 
      prevent, but it was inconvenient and impractical for patients to self-manage. 
      With this in mind, this study built a Bayesian network (BN) model, from the 
      perspective of diabetic patients' self-management and prevention, to predict six 
      complications of T2DM using the selected warning factors which patients could 
      have access from medical examination. Furthermore, the model was analyzed to 
      explore the relationships between physiological variables and T2DM complications, 
      as well as the complications themselves. The model aims to help patients with 
      T2DM self-manage and prevent themselves from complications. METHODS: The dataset 
      was collected from a well-known data center called the National Health Clinical 
      Center between 1st January 2009 and 31st December 2009. After preprocess and 
      impute the data, a BN model merging expert knowledge was built with Bootstrap and 
      Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors 
      and predict T2DM complications. Moreover, a Bayesian network without prior 
      information (BN-wopi) model learned using 10-fold cross-validation both in 
      structure and in parameters was added to compare with other classifiers learned 
      using 10-fold cross-validation fairly. The warning factors were selected 
      according the structure learned in each fold and were used to predict. Finally, 
      the performance of two BN models using warning features were compared with Naive 
      Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all 
      features to predict. Besides, the validation parameters of the proposed model 
      were also compared with those in existing studies using some other variables in 
      clinical data or biomedical data to predict T2DM complications. RESULTS: 
      Experimental results indicated that the BN models using warning factors performed 
      statistically better than their counterparts using all other variables in 
      predicting T2DM complications. In addition, the proposed BN model were effective 
      and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic 
      foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) 
      and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors 
      compared with other experiments. CONCLUSIONS: The warning factors of DN, DF, DMV, 
      and DK selected by MB in this research might be able to help predict certain T2DM 
      complications effectively, and the proposed BN model might be used as a general 
      tool for prevention, monitoring, and self-management.
CI  - Copyright (c) 2020. Published by Elsevier B.V.
FAU - Liu, Siying
AU  - Liu S
AD  - School of Economics and Management, Beijing Jiaotong University, Beijing 100044, 
      PR China.
FAU - Zhang, Runtong
AU  - Zhang R
AD  - School of Economics and Management, Beijing Jiaotong University, Beijing 100044, 
      PR China. Electronic address: rtzhang@bjtu.edu.cn.
FAU - Shang, Xiaopu
AU  - Shang X
AD  - School of Economics and Management, Beijing Jiaotong University, Beijing 100044, 
      PR China.
FAU - Li, Weizi
AU  - Li W
AD  - Informatics Research Center, University of Reading, Berkshire RG6 6AH, United 
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20200102
PL  - Ireland
TA  - Comput Methods Programs Biomed
JT  - Computer methods and programs in biomedicine
JID - 8506513
RN  - 0 (Biomarkers)
SB  - IM
MH  - Adult
MH  - Aged
MH  - Algorithms
MH  - Area Under Curve
MH  - *Bayes Theorem
MH  - Biomarkers/*analysis
MH  - Decision Trees
MH  - Diabetes Complications/diagnosis
MH  - Diabetes Mellitus, Type 2/*complications/*diagnosis
MH  - Female
MH  - Humans
MH  - Male
MH  - Markov Chains
MH  - Middle Aged
MH  - Probability
MH  - Reproducibility of Results
MH  - Self Care
MH  - Young Adult
OTO - NOTNLM
OT  - Bayesian network
OT  - Prevention
OT  - Self-management
OT  - Type 2 diabetes mellitus complications
OT  - Warning factors
COIS- Declaration of Competing Interest None.
EDAT- 2020/01/11 06:00
MHDA- 2021/02/09 06:00
CRDT- 2020/01/11 06:00
PHST- 2019/05/01 00:00 [received]
PHST- 2019/12/05 00:00 [revised]
PHST- 2019/12/24 00:00 [accepted]
PHST- 2020/01/11 06:00 [pubmed]
PHST- 2021/02/09 06:00 [medline]
PHST- 2020/01/11 06:00 [entrez]
AID - S0169-2607(19)30633-9 [pii]
AID - 10.1016/j.cmpb.2019.105302 [doi]
PST - ppublish
SO  - Comput Methods Programs Biomed. 2020 May;188:105302. doi: 
      10.1016/j.cmpb.2019.105302. Epub 2020 Jan 2.