Prenatal depression level prediction using ensemble based deep learning model

Prenatal depression level prediction using ensemble based deep learning model

Background and objective:
Many people find that the emotional and mental strain of labor and delivery is greater than they anticipated. However, there are few reports on stress levels during pregnancy, and there is limited research into stress observation during delivery. Prenatal depression during the delivery has to be monitored continuously without disturbing the mothers during the childbirth.
Methods:
We explore the potential of employing EDA for Prenatal Depression prediction. The proposed model applies a novel method for motion artifacts followed by data labeling using PHQ-9 score values and LOOCV applied to train robustly. This culminated in the development of a novel EBDL model to accurately predict stress levels.
Results:
We subsequently applied the ensemble based deep learning model on a testing dataset and our method proved to be 93.87 percent accurate, proving its superiority over the standard supervised classification models. The accuracy of this approach applied to three benchmark datasets produced better results compared to all commonly applied machine learning models, including an Ensemble based Deep Learning model.
Conclusion:
The preliminary results are promising, and indicate a superior utility of EDA for monitoring stress levels in real-life scenarios. This approach should be applied to a clinical setting, it potentially could continuously monitor stress levels in pregnant women and provide real-time feedback of clinically important data for clinicians.