A Comparative Review and Cycle-Aware Deep Learning Framework for Women’s Stress Prediction

Chronic stress in women is a growing global concern, influenced by complex physiological, behavioral, and hormonal interactions. Many existing stress-prediction systems fail to account for women's biological rhythms, resulting in weak generalization, calibration, and interpretability. This paper presents a comprehensive review of state-of-the-art machine learning (ML) and deep learning (DL) techniques for stress detection that use multimodal physiological and contextual inputs. The review identifies significant research gaps in hormone awareness, fairness, data imbalance, and privacy-preserving deployment. To address these gaps, a Cycle-Aware Deep Learning Framework (CADLF) is proposed, integrating five analytical components—Cycle-Conditioned Multimodal Contrastive Learning (CC-MCL), Hormone-Guided Neural Hawkes-TCN (HG-NHTCN), Fairness-Preserving Optimal-Transport Calibration (FOT-Cal), Front-Door Variational Context Network (FD-VCN), and Closed-Loop Digital-Twin Trials. The framework enables phase-aware feature extraction, fairness calibration, federated privacy, and causal validation. CADLF bridges theoretical and practical gaps in gender-specific stress modeling, providing a foundation for ethical, explainable, and personalized AI-based mental-health solutions.