Benchmarking machine learning architectures for menstrual recovery prediction using physiologically informed synthetic wearable data
Secondary amenorrhea is a heterogeneous condition with implications for reproductive, cardiovascular, and bone health. Existing machine learning approaches in menstrual health focus on cycle prediction rather than recovery modeling in pathological co...
Key Findings
Secondary amenorrhea is a heterogeneous condition with implications for reproductive, cardiovascular, and bone health. Existing machine learning approaches in menstrual health focus on cycle prediction rather than recovery modeling in pathological conditions. We present a proof-of-concept framework to model menstrual recovery within three months from non-invasive wearable-derived physiological features and self-reported inputs, including heart rate variability, resting heart rate, sleep, physical activity, skin temperature, perceived stress, age, and duration of amenorrhea. Using a synthetically generated dataset of 5000 individuals encoding physiologically informed feature-outcome relationships, twelve models were evaluated across baseline and longitudinal configurations. The best-performing model (XGBoost) achieved an AUC of 0.914, with ablation analysis confirming baseline features capturing the majority of learnable signal (ΔAUC = 0.020). Permutation-based null models confirmed non-trivial predictive structure (AUC = 0.503), and XGBoost outperformed rule-based baselines (ΔAUC = 0.044). SHAP analysis identified perceived stress and heart rate variability as dominant predictors, consistent with the data-generating structure. As the wearable-derived features are routinely captured by consumer devices and the self-reported inputs require brief periodic assessment, this framework establishes a foundation for wearable-based modeling of menstrual recovery, with future work required for real-world clinical validation and integration into health monitoring systems.