Predicting Risk of Perinatal Depression and Anxiety Using Biomedical, Psychosocial & Socioeconomic Factors Through Machine Learning

Abstract

Perinatal depression and anxiety are described to be the depression and anxiety a woman faces during pregnancy, around childbirth and after child delivery. While this often occurs and affects all family including the mother and infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal anxiety and depression worldwide and especially in low-income countries are extremely high. The wide majority where suffers from mild to moderate depression with the risk of leading to impaired child-mother relationship and infant health, few women end up taking their own lives. Owing to high costs and availability of resources associated with it, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on child and mother health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, specificity, precision, sensitivity, F1 score, and area under the ROC curve were used to evaluate the performance of the trained model. Multilayer perceptron and Support Vector Classifier achieved an area under the ROC curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital‟s gynaecology and obstetrics departments.