Synergistic Precision: Integrating Artificial Intelligence and Bioactive Natural Products for Advanced Prediction of Maternal Mental Health During Pregnancy

Abstract

Background: Present-day advancements in Artificial Intelligence (AI) and Machine Learning offer promising avenues for addressing psychological health challenges in pregnant women. Despite significant strides, there remains a gap in effective, accessible and personalised interventions for managing mental health risks during pregnancy, which can have profound implications on both maternal and fetal well-being. Aim: This study aims to develop a predictive model for monitoring and assessing the psychological health of pregnant women. The goal is to create an accessible tool using machine learning algorithms and sustainable technologies to provide early warnings and support interventions for mental health issues. Methods: The research utilised a dataset comprising psychological health indicators of pregnant women, including symptoms such as anxiety, depression and sleep disturbances. Machine Learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression and Gaussian Naive Bayes, were employed to classify mental health status. The models were evaluated using metrics like accuracy, precision, recall and F1-score. A web-based chatbot was developed to integrate the predictive model, providing real-time mental health assessments and personalised recommendations. Results: The Random Forest model demonstrated superior performance, achieving an accuracy of 92%, outperforming other models like SVM and Decision Tree, which achieved accuracies of 88% and 85%, respectively. Integrating the model into a web-based chatbot provided users with an interactive and user-friendly platform for mental health monitoring. Initial feedback from users indicated a 70% satisfaction rate with the tool’s ease of use and perceived accuracy. Conclusion: The study successfully developed a machine learning-based predictive model for assessing the psychological health of pregnant women, integrated into a web-based chatbot. This approach offers a promising, scalable solution for early detection and management of mental health challenges during pregnancy, potentially enhancing maternal and fetal outcomes through timely interventions.