Machine learning strategies for human stress level prediction: a comparative performance analysis

This review paper offers a thorough examination of current machine learning models for estimating human stress levels, with particular attention to Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks. A rigorous analysis of these models' performance, benefits, and drawbacks highlights their potential for prediction and stress management. The review examines how each model's architecture and features impact the predictability, accuracy, and interpretability of results. It identifies future objectives and research gaps, including the integration of multimodal data, emotion recognition, and explainable AI systems. This review aims to inform researchers and professionals involved in stress prediction and management to help develop more effective stress management solutions.