POSTPARTUM DEPRESSION PREDICTION USING MACHINE LEARNING

Abstract

After giving birth, postpartum depression is a serious mental health problem that affects moms. Symptoms may include trouble bonding with the child, difficulty sleeping, loss of appetite, and extreme irritability. Among 50 % of the mothers experience major changes in their mental health and approximately 1 in 10 women will seek help.The typical duration of postpartum depression is 3 to 6 months, however this might vary depending on a number of factors. The Edinburgh Postnatal Depression Scale (EPDS) data set, which is gathered after a week from the mothers who gave birth to their children, is used in this study to train the model using Random forest algorithm.This model has two phases, where the first phase predicts the status of the mother in any one of the four ranges (Depressed, Most Likely Depressed, Likely Depressed, No Depression) based on the trained data. Where the second phase predicts the level of depression(0-4 none, 5-9 mild, 10-14 Moderate, 15-19 moderately severe, 20- 27 severe.) based on the PHQ-9 Questionnaire, and it is suggested to take post-6 weeks of delivery. The results of phase one and two will be sent to the user via mail when they take the assessments in week one and week 6 respectively. The key benefit of this model is that there is no need for a third party because user and model evaluations take place directly between them. Since postpartum depression is not properly understood, many mothers do not receive treatment for their sickness, which can result in terrible circumstances and can be avoided by following this approach.