PMID- 31346994
OWN - NLM
STAT- MEDLINE
DCOM- 20191226
LR  - 20210110
IS  - 2523-899X (Electronic)
IS  - 2523-899X (Linking)
VI  - 39
IP  - 4
DP  - 2019 Aug
TI  - Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a 
      Cross-sectional Retrospective Study in Chinese Adults.
PG  - 582-588
LID - 10.1007/s11596-019-2077-4 [doi]
AB  - Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, 
      especially in urban areas. Early prevention strategies are needed to reduce the 
      associated mortality and morbidity. We applied the combination of rules and 
      different machine learning techniques to assess the risk of development of T2DM 
      in an urban Chinese adult population. A retrospective analysis was performed on 
      8000 people with non-diabetes and 3845 people with T2DM in Nanjing. Multilayer 
      Perceptron (MLP), AdaBoost (AD), Trees Random Forest (TRF), Support Vector 
      Machine (SVM), and Gradient Tree Boosting (GTB) machine learning techniques with 
      10 cross validation methods were used with the proposed model for the prediction 
      of the risk of development of T2DM. The performance of these models was evaluated 
      with accuracy, precision, sensitivity, specificity, and area under receiver 
      operating characteristic (ROC) curve (AUC). After comparison, the prediction 
      accuracy of the different five machine models was 0.87, 0.86, 0.86, 0.86 and 0.86 
      respectively. The combination model using the same voting weight of each 
      component was built on T2DM, which was performed better than individual models. 
      The findings indicate that, combining machine learning models could provide an 
      accurate assessment model for T2DM risk prediction.
FAU - Xiong, Xiao-Lu
AU  - Xiong XL
AD  - Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of 
      Nanjing Medical University, Nanjing, 210008, China.
FAU - Zhang, Rong-Xin
AU  - Zhang RX
AD  - School of Biomedical Engineering and Informatics, Nanjing Medical University, 
      Nanjing, 211166, China.
FAU - Bi, Yan
AU  - Bi Y
AD  - Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of 
      Nanjing Medical University, Nanjing, 210008, China.
FAU - Zhou, Wei-Hong
AU  - Zhou WH
AD  - Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of 
      Nanjing Medical University, Nanjing, 210008, China. njzhouweihong@126.com.
FAU - Yu, Yun
AU  - Yu Y
AD  - School of Biomedical Engineering and Informatics, Nanjing Medical University, 
      Nanjing, 211166, China. yuyun@njmu.edu.cn.
FAU - Zhu, Da-Long
AU  - Zhu DL
AD  - Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of 
      Nanjing Medical University, Nanjing, 210008, China. zhudalong@nju.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20190725
PL  - China
TA  - Curr Med Sci
JT  - Current medical science
JID - 101729993
SB  - IM
MH  - Adult
MH  - China/epidemiology
MH  - Cross-Sectional Studies
MH  - Diabetes Mellitus, Type 2/diagnosis/*epidemiology/pathology
MH  - Female
MH  - Humans
MH  - *Machine Learning
MH  - Male
MH  - Retrospective Studies
MH  - *Risk Assessment
OTO - NOTNLM
OT  - machine learning
OT  - risk prediction
OT  - type 2 diabetes
EDAT- 2019/07/28 06:00
MHDA- 2019/12/27 06:00
CRDT- 2019/07/27 06:00
PHST- 2018/07/17 00:00 [received]
PHST- 2019/06/10 00:00 [revised]
PHST- 2019/07/27 06:00 [entrez]
PHST- 2019/07/28 06:00 [pubmed]
PHST- 2019/12/27 06:00 [medline]
AID - 10.1007/s11596-019-2077-4 [pii]
AID - 10.1007/s11596-019-2077-4 [doi]
PST - ppublish
SO  - Curr Med Sci. 2019 Aug;39(4):582-588. doi: 10.1007/s11596-019-2077-4. Epub 2019 
      Jul 25.