Enhancing Mental Health Interventions in the USA with Semi-Supervised Learning: An AI Approach to Emotion Prediction

Abstract

The escalating prevalence of mental health challenges in the USA underscores the urgent need for innovative resolutions to enhance interventions and care. Accurate prediction of emotional states can empower mental health practitioners to provide timely and personalized support. The main objective of this study was to develop and evaluate semi-supervised learning models for emotion prediction in mental health. The present study's prime focus is applying semi-supervised learning in the U.S. context to mental health datasets. The Emotion Prediction Dataset is one of the diverse datasets collected from different sources with the aim of gaining a wide understanding of emotional state conditions. It includes text data from social media platforms, such as Twitter and Facebook, where users express their feelings right at the moment; audio recordings from speech and interactions that capture vocal nuances and intonation; and physiological signals captured through wearable devices measuring heart rate, skin conductance, and facial electromyography. Logistic Regression, Random Forest, and Gradient Boosting are some of the models considered in this study. Model evaluation executed proven metrics such as accuracy, precision, recall, and F1-Score assesses performance comprehensively. Although all three models generally performed worse, the SVM model provided the most reliable predictions in the context of this dataset and may, therefore, be effective for emotion classification. Integrating emotion prediction models into existing mental health services offers a new paradigm in patient care. A strong framework for such integration should start with an assessment of the current platforms, highlighting key points where emotion prediction can complement the existing services. Emotion prediction models can significantly enhance support strategies by targeting interventions at predicted emotional changes. Mental health professionals will be able to create personal treatment plans in which the trends within the data denote specific emotional states the patient is most likely to experience. The consolidation of AI-powered emotion prediction algorithms into mental health services in the USA carries substantial ramifications for improving the quality of care and accessibility of mental health resources.