PMID- 37107975
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230430
IS  - 2227-9032 (Print)
IS  - 2227-9032 (Electronic)
IS  - 2227-9032 (Linking)
VI  - 11
IP  - 8
DP  - 2023 Apr 15
TI  - Using Machine Learning for the Risk Factors Classification of Glycemic Control in 
      Type 2 Diabetes Mellitus.
LID - 10.3390/healthcare11081141 [doi]
LID - 1141
AB  - Several risk factors are related to glycemic control in patients with type 2 
      diabetes mellitus (T2DM), including demographics, medical conditions, negative 
      emotions, lipid profiles, and heart rate variability (HRV; to present cardiac 
      autonomic activity). The interactions between these risk factors remain unclear. 
      This study aimed to use machine learning methods of artificial intelligence to 
      explore the relationships between various risk factors and glycemic control in 
      T2DM patients. The study utilized a database from Lin et al. (2022) that included 
      647 T2DM patients. Regression tree analysis was conducted to identify the 
      interactions among risk factors that contribute to glycated hemoglobin (HbA1c) 
      values, and various machine learning methods were compared for their accuracy in 
      classifying T2DM patients. The results of the regression tree analysis revealed 
      that high depression scores may be a risk factor in one subgroup but not in 
      others. When comparing different machine learning classification methods, the 
      random forest algorithm emerged as the best-performing method with a small set of 
      features. Specifically, the random forest algorithm achieved 84% accuracy, 95% 
      area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine 
      learning methods can provide significant value in accurately classifying patients 
      with T2DM when considering depression as a risk factor.
FAU - Cheng, Yi-Ling
AU  - Cheng YL
AUID- ORCID: 0000-0002-7484-1916
AD  - Department of Psychology, College of Humanities and Social Sciences, Kaohsiung 
      Medical University, Kaohsiung 807378, Taiwan.
FAU - Wu, Ying-Ru
AU  - Wu YR
AD  - Department of Psychology, College of Humanities and Social Sciences, Kaohsiung 
      Medical University, Kaohsiung 807378, Taiwan.
FAU - Lin, Kun-Der
AU  - Lin KD
AD  - The Lin's Clinic, Kaohsiung 807057, Taiwan.
FAU - Lin, Chun-Hung Richard
AU  - Lin CR
AUID- ORCID: 0000-0003-0840-394X
AD  - Department of Computer Science and Engineering, National Sun Yat-sen University, 
      Kaohsiung 80424, Taiwan.
FAU - Lin, I-Mei
AU  - Lin IM
AUID- ORCID: 0000-0001-9658-1783
AD  - Department of Psychology, College of Humanities and Social Sciences, Kaohsiung 
      Medical University, Kaohsiung 807378, Taiwan.
AD  - Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 
      807378, Taiwan.
LA  - eng
GR  - NSYSUKMU111KN-002/Kaohsiung Medical University/
GR  - MOST-111-2410-H-037-004-MY2/Ministry of Science and Technology, Taiwan/
PT  - Journal Article
DEP - 20230415
PL  - Switzerland
TA  - Healthcare (Basel)
JT  - Healthcare (Basel, Switzerland)
JID - 101666525
PMC - PMC10138388
OTO - NOTNLM
OT  - artificial intelligence
OT  - depression
OT  - glycemic control
OT  - machine learning
OT  - type 2 diabetes mellitus
COIS- The authors declare no conflict of interest. The funders had no role in the 
      design of the study; in the collection, analyses, or interpretation of data; in 
      the writing of the manuscript; or in the decision to publish the results.
EDAT- 2023/04/28 06:41
MHDA- 2023/04/28 06:42
PMCR- 2023/04/15
CRDT- 2023/04/28 01:24
PHST- 2023/03/06 00:00 [received]
PHST- 2023/04/05 00:00 [revised]
PHST- 2023/04/13 00:00 [accepted]
PHST- 2023/04/28 06:42 [medline]
PHST- 2023/04/28 06:41 [pubmed]
PHST- 2023/04/28 01:24 [entrez]
PHST- 2023/04/15 00:00 [pmc-release]
AID - healthcare11081141 [pii]
AID - healthcare-11-01141 [pii]
AID - 10.3390/healthcare11081141 [doi]
PST - epublish
SO  - Healthcare (Basel). 2023 Apr 15;11(8):1141. doi: 10.3390/healthcare11081141.