<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Objective</h3>
<p>To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy.</p>
</sec>
<sec><h3>Methods</h3>
<p>This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed.</p>
</sec>
<sec><h3>Results</h3>
<p>We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.</p>
</sec>
</abstract>

Objective
To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy.

Methods
This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed.

Results
We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%.

Conclusions
In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy<xref ref-type="fn" rid="j_fon-2021-0022_fn_001"><sup>†</sup></xref>

The Affiliated Hospital of Qingdao University

Research Institute for Mathematics and Interdisciplinary Sciences

Qingdao University of Science and Technology

Department of Critical Care and Respiratory Medicine

Frontiers of Nursing

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy

Objective
To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy.

Methods
This study...

Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy^†

Article Category: Original article

Pubblicato online: 21 set 2021

Pagine: 209 - 221

Ricevuto: 24 nov 2020

Accettato: 11 gen 2021

DOI: https://doi.org/10.2478/fon-2021-0022

Parole chiave
early prediction, gestational diabetes mellitus, machine learning algorithm, random forest regression

© 2021 Li-Li Wei et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

Article Category: Original article

Pubblicato online: 21 set 2021

Pagine: 209 - 221

Ricevuto: 24 nov 2020

Accettato: 11 gen 2021

DOI: https://doi.org/10.2478/fon-2021-0022

Parole chiaveearly prediction, gestational diabetes mellitus, machine learning algorithm, random forest regression

© 2021 Li-Li Wei et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy^†

Parole chiave
early prediction, gestational diabetes mellitus, machine learning algorithm, random forest regression