Perinatal Mental Detection With Chatbots Using Machine Learning

Abstract

The use of human-robot interaction in mental health has received significant attention. As opposed to the traditional approach, using robots for mental health care reduces subjects' barriers in seeking help for their own mental conditions and obtains a more comprehensive data of patients that can aid users being aware of their state on a broader scale but also serves as support to clinicians towards diagnosing with better accuracy. This paper is about a chatbot to check the mental status of perinatal women. This paper applies supervised machine learning on a dataset comprised of 223 samples having 31 features to build predictive model for detecting Anxiety, Depression and Hypomania index in perinatal woman. Technical, psychological scales is accessed for the purpose of evaluation and to provide suggestions in terms of treatment which would help users recover from their mental illness. Perinatal mental health (PMH) problems are mood disorders that occur during pregnancy or up to 24-month postpartum period and can have far reaching effects on pregnant women, baby's wellbeing; the quality of family partnerships may be affected. Problems that a woman could face at any stage of her pregnancy Most used methods in the PMH diagnosis were self-reporting, behavioral scale testing and behavioral observation. To keep up with this, Chatbot is a good way. By collecting data on user health and communicating with humans, it can provide real-time perinatal mental health care.