Comparison of gestational diabetes prediction with artificial neural network and decision tree models

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

REZAEI MANSOUR; Fakhri Negin; Rajati Fateme; SHAHSAVARI SOODEH

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Tehran University Medical Journal Year:2019 | Volume:77 | Issue:6 Page(s): 359-367

Download Full-Text

Persian Verion

View:

518

Download:

Cites:

Information Journal Paper

Title

Comparison of gestational diabetes prediction with artificial neural network and decision tree models

Author(s)

REZAEI MANSOUR | Fakhri Negin | Rajati Fateme | SHAHSAVARI SOODEH | Issue Writer Certificate

Keywords

gestational diabetes mellitusQ2

artificial neural networkQ2

decision treeQ1

accuracyQ2

sensitivityQ3

Abstract

Background: gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM. Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced. Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0. 83, 0. 77, the sensitivity 0. 62, 0. 56 and specificity 0. 95, 0. 87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0. 79, 0. 74, P=0. 03). Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

Cites

No record.

References

No record.

Cite

APA: Copy

REZAEI, MANSOUR, Fakhri, Negin, Rajati, Fateme, & SHAHSAVARI, SOODEH. (2019). Comparison of gestational diabetes prediction with artificial neural network and decision tree models. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), 77(6 ), 359-367. SID. https://sid.ir/paper/364653/en

Vancouver: Copy

REZAEI MANSOUR, Fakhri Negin, Rajati Fateme, SHAHSAVARI SOODEH. Comparison of gestational diabetes prediction with artificial neural network and decision tree models. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ)[Internet]. 2019;77(6 ):359-367. Available from: https://sid.ir/paper/364653/en

IEEE: Copy

MANSOUR REZAEI, Negin Fakhri, Fateme Rajati, and SOODEH SHAHSAVARI, “Comparison of gestational diabetes prediction with artificial neural network and decision tree models,” TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), vol. 77, no. 6 , pp. 359–367, 2019, [Online]. Available: https://sid.ir/paper/364653/en

Related Journal Papers