A neural network model for preeclampsia prediction based on risk factors
by Masoumeh Mirzamoradi; Atefeh Ebrahimi; Ali Ameri; Masoumeh Abaspour; Hamid Mokhtari Torshizi
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 14, No. 4, 2022

Abstract: This study proposes a risk factor-based neural network model for preeclampsia prediction during the second trimester of pregnancy. A total of 320 women giving birth (160 normal delivery, 160 with preeclampsia) at Mahdieh Gynecology Hospital during 2018-2019 were inquired for 13 risk factors. Data from 85% of the subjects (selected randomly) were employed to train the network and data from the remaining subjects were used to test the performance of the model. This process was repeated 100 times and the average results were determined. The proposed model achieved an accuracy of 83% in classifying the subjects into normal and preeclampsia classes, based on the risk factors input data, with a sensitivity of 83% and a specificity of 82%.

Online publication date: Tue, 05-Jul-2022

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