PMID- 35885524
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220731
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 12
IP  - 7
DP  - 2022 Jul 3
TI  - Comparison between Machine Learning and Multiple Linear Regression to Identify 
      Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.
LID - 10.3390/diagnostics12071619 [doi]
LID - 1619
AB  - Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery 
      disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a 
      non-invasive and extensively used tool in recognizing CAD in clinical settings. 
      In this study, we attempted to compare the predictive accuracy of evaluating 
      abnormal Th-201 scans using traditional multiple linear regression (MLR) with 
      four machine learning (ML) methods. From the study, we can determine whether ML 
      surpasses traditional MLR and rank the clinical variables and compare them with 
      previous reports.In total, 796 T2DM, including 368 men and 528 women, were 
      enrolled. In addition to traditional MLR, classification and regression tree 
      (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme 
      gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. 
      Stress sum score was used as the endpoint (dependent variable). Our findings show 
      that all four root mean square errors of ML are smaller than with MLR, which 
      implies that ML is more precise than MLR in determining abnormal Th-201 scans by 
      using clinical parameters. The first seven factors, from the most important to 
      the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, 
      systolic and diastolic blood pressure. In conclusion, ML is not inferior to 
      traditional MLR in predicting abnormal Th-201 scans, and the most important 
      factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, 
      systolic and diastolic blood pressure. ML methods are superior in these kinds of 
      studies.
FAU - Lin, Jiunn-Diann
AU  - Lin JD
AD  - Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, 
      Taipei Medical University, New Taipei City 23651, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, School 
      of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, 
      Taiwan.
FAU - Pei, Dee
AU  - Pei D
AD  - School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 
      24205, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen 
      Catholic University Hospital, New Taipei 24352, Taiwan.
FAU - Chen, Fang-Yu
AU  - Chen FY
AD  - School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 
      24205, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen 
      Catholic University Hospital, New Taipei 24352, Taiwan.
FAU - Wu, Chung-Ze
AU  - Wu CZ
AUID- ORCID: 0000-0001-6118-6070
AD  - Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, 
      Taipei Medical University, New Taipei City 23651, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, School 
      of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, 
      Taiwan.
FAU - Lu, Chieh-Hua
AU  - Lu CH
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, 
      Tri-Service General Hospital, School of Medicine, National Defense Medical 
      Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, 
      Taiwan.
FAU - Huang, Li-Ying
AU  - Huang LY
AD  - School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 
      24205, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen 
      Catholic University Hospital, New Taipei 24352, Taiwan.
FAU - Kuo, Chun-Heng
AU  - Kuo CH
AUID- ORCID: 0000-0001-7673-3567
AD  - School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 
      24205, Taiwan.
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen 
      Catholic University Hospital, New Taipei 24352, Taiwan.
FAU - Kuo, Shi-Wen
AU  - Kuo SW
AUID- ORCID: 0000-0002-0051-2407
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei 
      Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289, Jianguo Rd., 
      Xindian Dist., New Taipei City 23142, Taiwan.
FAU - Chen, Yen-Lin
AU  - Chen YL
AD  - Department of Pathology, Tri-Service General Hospital, National Defense Medical 
      Center, No. 325. Sec. 2, Chenggong Rd., Neihu District, Taipei City 11490, 
      Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20220703
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC9324130
OTO - NOTNLM
OT  - coronary artery disease
OT  - machine learning
OT  - thallium-201 myocardial perfusion scan
OT  - type 2 diabetes mellitus
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/28 06:00
MHDA- 2022/07/28 06:01
PMCR- 2022/07/03
CRDT- 2022/07/27 01:15
PHST- 2022/06/04 00:00 [received]
PHST- 2022/06/27 00:00 [revised]
PHST- 2022/06/30 00:00 [accepted]
PHST- 2022/07/27 01:15 [entrez]
PHST- 2022/07/28 06:00 [pubmed]
PHST- 2022/07/28 06:01 [medline]
PHST- 2022/07/03 00:00 [pmc-release]
AID - diagnostics12071619 [pii]
AID - diagnostics-12-01619 [pii]
AID - 10.3390/diagnostics12071619 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2022 Jul 3;12(7):1619. doi: 10.3390/diagnostics12071619.