Development of personalized machine learning models for predicting panic attacks in college students

Panic attacks (PAs) are unexpected episodes of intense fear and anxiety, accompanied by cognitive and physical symptoms that significantly impair day-to-day functioning. This study proposes a machine learning approach to predict PA risk in the upcoming week in individuals across mental health conditions, enabling timely and potentially preventive mental health intervention. We evaluate the approach in a longitudinal sample of behavioral and physiological data collected using Oura rings from 39 college students over six months. We assessed multiple prediction algorithms applied within both pers