Early Risk Detection of Pre-eclampsia for Pregnant Women Using Artificial Neural Network

Endah Purwanti, Ichroom Septa Preswari, Ernawati Ernawati


Pre-eclampsia still dominates maternal mortality cases in Indonesia. One effort that can be done is to establish early detection of the risk of pre-eclampsia in pregnant women. Automated devices with high accuracy are needed to detect the risk of pre-eclampsia so that the maternal mortality ratio can be reduced. This study aims to design an early detection system for the risk of pre-eclampsia based on artificial neural networks. The system is designed with 11 input parameters in the form of risk factors and output in the form of positive or negative risk of pre-eclampsia. The classification tool used in this study is backpropagation neural network with cross validation scenario at the training stage. The advantage of this system is the weighting of risk factor parameters by obstetric and gynecology specialists so that the results of testing the device show high accuracy. In addition, the device for early detection of pre-eclampsia was also conducted by user acceptance tests for a number of pregnant women.

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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