PMID- 35250610
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220308
IS  - 1664-042X (Print)
IS  - 1664-042X (Electronic)
IS  - 1664-042X (Linking)
VI  - 13
DP  - 2022
TI  - Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a 
      History of Gestational Diabetes Mellitus Through Machine-Learning Techniques.
PG  - 789219
LID - 10.3389/fphys.2022.789219 [doi]
LID - 789219
AB  - Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves 
      at the end of the pregnancy but exposes to a higher risk of developing type 2 
      diabetes mellitus (T2DM). This study aimed to unravel the factors, among those 
      that quantify specific metabolic processes, which determine progression to T2DM 
      by using machine-learning techniques. Classification of women who did progress to 
      T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 
      59) progress to T2DM has been performed by using Orange software through a data 
      analysis procedure on a generated data set including anthropometric data and a 
      total of 34 features, extracted through mathematical modeling/methods procedures. 
      Feature selection has been performed through decision tree algorithm and then 
      Naive Bayes and penalized (L2) logistic regression were used to evaluate the 
      ability of the selected features to solve the classification problem. Performance 
      has been evaluated in terms of area under the operating receiver characteristics 
      (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. 
      Feature selection provided six features, and based on them, classification was 
      performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 
      0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 
      0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 
      for tree algorithm, Naive Bayes, and penalized logistic regression, respectively. 
      Fasting glucose, age, and body mass index together with features describing 
      insulin action and secretion may predict the development of T2DM in women with a 
      history of GDM.
CI  - Copyright (c) 2022 Ilari, Piersanti, Gobl, Burattini, Kautzky-Willer, Tura and 
      Morettini.
FAU - Ilari, Ludovica
AU  - Ilari L
AD  - Department of Information Engineering, Universita Politecnica delle Marche, 
      Ancona, Italy.
FAU - Piersanti, Agnese
AU  - Piersanti A
AD  - Department of Information Engineering, Universita Politecnica delle Marche, 
      Ancona, Italy.
FAU - Gobl, Christian
AU  - Gobl C
AD  - Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, 
      Austria.
FAU - Burattini, Laura
AU  - Burattini L
AD  - Department of Information Engineering, Universita Politecnica delle Marche, 
      Ancona, Italy.
FAU - Kautzky-Willer, Alexandra
AU  - Kautzky-Willer A
AD  - Division of Endocrinology and Metabolism, Department of Internal Medicine III, 
      Medical University of Vienna, Vienna, Austria.
FAU - Tura, Andrea
AU  - Tura A
AD  - Metabolic Unit, CNR Institute of Neuroscience, Padua, Italy.
FAU - Morettini, Micaela
AU  - Morettini M
AD  - Department of Information Engineering, Universita Politecnica delle Marche, 
      Ancona, Italy.
LA  - eng
PT  - Journal Article
DEP - 20220217
PL  - Switzerland
TA  - Front Physiol
JT  - Frontiers in physiology
JID - 101549006
PMC - PMC8892139
OTO - NOTNLM
OT  - disease prediction
OT  - logistic regression
OT  - mathematical model
OT  - pathophysiology
OT  - predictive biomarker
OT  - statistical learning
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- 2022/03/08 06:00
MHDA- 2022/03/08 06:01
PMCR- 2022/02/17
CRDT- 2022/03/07 06:02
PHST- 2021/10/04 00:00 [received]
PHST- 2022/01/11 00:00 [accepted]
PHST- 2022/03/07 06:02 [entrez]
PHST- 2022/03/08 06:00 [pubmed]
PHST- 2022/03/08 06:01 [medline]
PHST- 2022/02/17 00:00 [pmc-release]
AID - 10.3389/fphys.2022.789219 [doi]
PST - epublish
SO  - Front Physiol. 2022 Feb 17;13:789219. doi: 10.3389/fphys.2022.789219. eCollection 
      2022.