PMID- 37396650
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230704
IS  - 1663-4365 (Print)
IS  - 1663-4365 (Electronic)
IS  - 1663-4365 (Linking)
VI  - 15
DP  - 2023
TI  - Development and validation of an interpretable machine learning model-Predicting 
      mild cognitive impairment in a high-risk stroke population.
PG  - 1180351
LID - 10.3389/fnagi.2023.1180351 [doi]
LID - 1180351
AB  - BACKGROUND: Mild cognitive impairment (MCI) is considered a preclinical stage of 
      Alzheimer's disease (AD). People with MCI have a higher risk of developing 
      dementia than healthy people. As one of the risk factors for MCI, stroke has been 
      actively treated and intervened. Therefore, selecting the high-risk population of 
      stroke as the research object and discovering the risk factors of MCI as early as 
      possible can prevent the occurrence of MCI more effectively. METHODS: The Boruta 
      algorithm was used to screen variables, and eight machine learning models were 
      established and evaluated. The best performing models were used to assess 
      variable importance and build an online risk calculator. Shapley additive 
      explanation is used to explain the model. RESULTS: A total of 199 patients were 
      included in the study, 99 of whom were male. Transient ischemic attack (TIA), 
      homocysteine, education, hematocrit (HCT), diabetes, hemoglobin, red blood cells 
      (RBC), hypertension, prothrombin time (PT) were selected by Boruta algorithm. 
      Logistic regression (AUC = 0.8595) was the best model for predicting MCI in 
      high-risk groups of stroke, followed by elastic network (ENET) (AUC = 0.8312), 
      multilayer perceptron (MLP) (AUC = 0.7908), extreme gradient boosting (XGBoost) 
      (AUC = 0.7691), and support vector machine (SVM) (AUC = 0.7527), random forest 
      (RF) (AUC = 0.7451), K-nearest neighbors (KNN) (AUC = 0.7380), decision tree (DT) 
      (AUC = 0.6972). The importance of variables suggests that TIA, diabetes, 
      education, and hypertension are the top four variables of importance. CONCLUSION: 
      Transient ischemic attack (TIA), diabetes, education, and hypertension are the 
      most important risk factors for MCI in high-risk groups of stroke, and early 
      intervention should be performed to reduce the occurrence of MCI.
CI  - Copyright (c) 2023 Yan, Chen, Quan, Wang, Wei and Zhu.
FAU - Yan, Feng-Juan
AU  - Yan FJ
AD  - Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, 
      Guangdong, China.
FAU - Chen, Xie-Hui
AU  - Chen XH
AD  - Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, 
      Guangdong, China.
FAU - Quan, Xiao-Qing
AU  - Quan XQ
AD  - Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, 
      Guangdong, China.
FAU - Wang, Li-Li
AU  - Wang LL
AD  - Department of Cardiology, Affiliated Hospital of Shandong University of 
      Traditional Chinese Medicine, Jinan, Shandong, China.
FAU - Wei, Xin-Yi
AU  - Wei XY
AD  - Department of Cardiology, The Third Hospital of Jinan, Jinan, Shandong, China.
FAU - Zhu, Jia-Liang
AU  - Zhu JL
AD  - The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
LA  - eng
PT  - Journal Article
DEP - 20230615
PL  - Switzerland
TA  - Front Aging Neurosci
JT  - Frontiers in aging neuroscience
JID - 101525824
PMC - PMC10308219
OTO - NOTNLM
OT  - Boruta algorithm
OT  - high-risk stroke population
OT  - machine learning
OT  - mild cognitive impairment
OT  - prediction model
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/07/03 13:06
MHDA- 2023/07/03 13:07
PMCR- 2023/01/01
CRDT- 2023/07/03 11:23
PHST- 2023/03/06 00:00 [received]
PHST- 2023/05/30 00:00 [accepted]
PHST- 2023/07/03 13:07 [medline]
PHST- 2023/07/03 13:06 [pubmed]
PHST- 2023/07/03 11:23 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fnagi.2023.1180351 [doi]
PST - epublish
SO  - Front Aging Neurosci. 2023 Jun 15;15:1180351. doi: 10.3389/fnagi.2023.1180351. 
      eCollection 2023.