Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR Mhealth Uhealth 2022;10(8):e33850. DOI: 10.2196/33850
This study aimed to develop and evaluate a machine learning model to predict next-day physiological and prenatal stress by collecting sensor heart rate data and ecological momentary assessment (EMA) questionnaires. This study applied an explainability model for the prediction results. A total of 16 adult pregnant women from an obstetrics and gynecology clinic were enrolled in the study. Participants received a 12-week cognitive behavioral therapy intervention and wore a mobile electrocardiography (heart rate) sensor for 12 weeks. Participants completed EMAs for perceived stress on their mobile phones 5 times a day for 12 weeks. In total, about 4000 hours of data were collected and participants completed 2800 EMAs. Researchers used these data to train and evaluate 6 different machine learning models to select the best performing model for predicting next-day physiological and perceived stress. The random forest classifier performed the best for both physiological and perceived stress, with an average F1 score (a commonly used evaluation metric) of 81.9% and 72.5%, respectively. Two features significantly predicted both physiological and perceived stress: feeling unable to overcome difficulties and participants’ number of children. Results demonstrated that a machine learning model can predict next-day physiological and perceived stress among pregnant women. Future studies should validate the model with a larger sample size.